search for yield in large international corporate …...firms contemplating whether to take...
TRANSCRIPT
Search for Yield in Large International Corporate Bonds:
Investor Behavior and Firm Responses
Charles W. Calomiris Mauricio Larrain Sergio L. Schmukler Tomas Williams*
May 30, 2019
Abstract Emerging market corporations have significantly increased their borrowing in international markets since 2008. We show that this increase was driven by large-denomination bond issuances, most of them with face value of exactly US$500 million. Large issuances are eligible for inclusion in important international market indexes. These bonds appeal to institutional investors because they are more liquid and facilitate targeting market benchmarks. We find that the rewards of issuing index-eligible bonds rose drastically after 2008. Emerging market firms were able to cut their cost of funds by roughly 100 basis points by issuing bonds with a face value greater than US$500 million relative to smaller bonds. Firms contemplating whether to take advantage of this cost savings faced a tradeoff: they benefitted from the 100 basis point lower yield associated with large, index-eligible bonds, but they paid the potential cost of having to hoard low-yielding cash assets if their investment opportunities were less than US$500 million. Because of the post-2008 “size yield discount,” many companies issued index-eligible bonds, while substantially increasing their cash holdings. The willingness to issue large bonds and hoard cash was greater for small firms in countries with high carry trade opportunities that reduced the cost of holding cash. We present evidence suggesting that these post-2008 behaviors reflected a search for yield by institutional investors into higher-risk securities. These patterns are not apparent in the issuance of investment grade bonds by firms in developed economies. JEL Classification Codes: F21, F23, F32, F36, F65, G11, G15, G31 Keywords: benchmark indexes, bond issuance, corporate financing, emerging markets, institutional investors
* Calomiris: Columbia Business School, Hoover Institution, and NBER, email: [email protected]; Larrain: Universidad Catolica de Chile School of Management and Financial Market Commission of Chile, email: [email protected]; Schmukler: World Bank Research Department, email: [email protected]. Williams: George Washington University, email: [email protected]. We thank Anil Kashyap, Jesse Schreger (discussant), and participants at presentations held at the 2019 ASSA Meetings, Stanford University, Colorado State University, Columbia Business School, George Washington Business School, International Monetary Fund, and Sao Paulo School of Economics (FGV) for useful comments. We are grateful to Facundo Abraham, Belinda Chen, Juan Cortina, Marta Guasch Rusiñol, and Soha Ismail for excellent research assistance, and to Tatiana Didier for facilitating access to data. We also thank Zhengyang Jiang and Arvind Krishnamurthy for sharing their data on foreign safe asset demand. The World Bank Chile Research & Development Center, Knowledge for Change Program (KCP), and Strategic Research Program (SRP) provided support for this paper.
1
1. Introduction
After the 2008 global financial crisis (GFC), interest rates in developed countries reached
historically low levels, especially for safe assets. Several studies argue that persistently low
interest rates on safe assets have led investors to search for yield by expanding the range
of investments they consider and by making them willing to accept increases in risk. As a
consequence, the search for yield has expanded the demand for emerging market securities,
especially corporate bonds issued in international markets (Becker and Ivashina, 2015;
Bruno and Shin, 2017).1
Because the international market for debt securities is dominated by institutional
investors, who face limits in their incentives or ability to undertake risk in unfamiliar asset
classes, the search for yield does not entail an unlimited willingness to accept new risks as
the demand for emerging market corporate debt rises. One way to limit risk, while
expanding investments into emerging market corporate debt, is to demand liquid
instruments. These securities allow investors to more easily sell positions when needed or
to increase them when desired, with minimal price impact and low transaction costs. Also,
institutional investors are often penalized with withdrawals or rewarded with inflows by
the ultimate investors (who are the principals in those investments). This disciplining
mechanism encourages managers to think of the risk that affects them (as agents) in terms
of deviations from the market benchmark indexes.
By purchasing bonds that are included in major indexes, institutional investors
both enhance liquidity and limit the risk of underperforming relevant indexes.2 Bonds that
1 We use the phrase “search for yield” to describe either (1) a broadening of the range of investments by institutional investors (e.g., U.S. corporate bond funds) to include riskier (emerging market corporate) bonds, or (2) decisions by ultimate individual investors to allocate more of their portfolios to riskier investments (e.g., emerging market bond funds). 2 There have been several studies that document that institutional investors such as mutual funds do not deviate too much from their respective indexes. See Cremers and Petajisto (2009) for evidence on the U.S. equity mutual fund industry. Cremers et al. (2016) and Raddatz et al. (2017) show this pattern at the international level. An extreme instance of this strategy is that used by exchange-traded funds (ETFs), the importance of which has increased recently, as documented by Converse et al. (2018).
2
are included in market indexes are bought and sold more frequently and are held by a wide
range of investors, which means that holding a bond that is included in the index enhances
its liquidity. Bonds that are included in the index collectively define the benchmark of
market performance, which means that holding those bonds limits an institutional
investor’s risk of underperforming the market benchmark.
Two of the most relevant benchmark indexes for emerging market bonds are the
J.P. Morgan EMBI Global Diversified Index (which focuses on sovereign bonds) and the
J.P. Morgan CEMBI Narrow Diversified Index (which focuses on corporate bonds).3 Both
indexes include bonds based on certain security attributes, notably the amount of
outstanding debt. Thus, only debt issues with face value greater or equal than $500
(US$500) million are included in these indexes. A broader index (the CEMBI Broad) also
exists, which includes corporate debt with face value greater than $300 million.
Because of their advantages, some institutional investors that expand their holdings
of emerging market corporate debt purchase bonds that are included in the major market
indexes. This means purchasing large bonds. One would expect that this preference would
increase bond prices through an inclusion premium and reduce bond yields, an effect that
we label the “size yield discount.” Also, one would expect that this preference would
increase the likelihood of issuing large bonds, as firms participating in international bond
markets take advantage of cheaper financing costs.
In this paper, we analyze how the change in global market conditions after 2008
interacted with market structure to affect the size and pricing of U.S. dollar-denominated
bonds issued by emerging market corporations. Specifically, we analyze a period where the
low interest rate environment created by developed countries’ monetary policies after the
GFC interacted with preferences of international investors that follow rules governing the
3 EMBI stands for Emerging Market Bond Index and CEMBI stands for Corporate Emerging Market Bond Index.
3
inclusion of bonds in debt market indexes. We also study how these changes affected firm
financing decisions and cash holdings.
Our first novel finding is that the expansion in the demand for emerging market
corporate debt was accompanied by an increased preference for bonds large enough to be
included in market indexes. After the GFC, we observe a substantial reduction in the yields
of bonds issued in international markets with a face value of $500 million, relative to
otherwise similar bonds with lower face value. For example, emerging market corporates
pay about 100 basis points less than they do prior to 2008 when issuing $500 million bonds
instead of $400 million bonds. In other words, the size yield discount increased
substantially after 2008. Not only did the average yields of bonds with face value of $500
million significantly decrease relative to the period before the GFC, but also this pattern is
much more visible for emerging market issuers than for developed market firms
(considered relatively safe investments).
Our second new finding is that in the post-2008 period, emerging market firms
were much more likely to issue debt securities in international markets with a face value of
exactly $500 million. When deciding to issue a large, index-eligible bond, firms face a trade-
off. On the one hand, they can secure cheaper financing costs. On the other hand, if
issuance size exceeds financing needs, firms have to save the difference in cash or cash-
like instruments, which have low returns. Our second finding suggests that after the GFC,
the increase in the size yield discount moved the trade-off in favor of issuing $500 million
bonds. Some firms chose to issue more than they needed to fund their projects in order
to reach the $500 million threshold, and hold cash assets from the proceeds of bond
issuance in excess of project funding needs. In addition, we show that higher interest rates
earned by firms’ on their cash assets encourage them to issue large bonds. We find that
firms in countries with higher expected carry trade (our proxy for return on cash) issue
4
more $500 million bonds, providing further evidence that firms respond to a trade-off
when deciding to issue large bonds in amounts that exceed their funding needs..
We present suggestive evidence that the channel driving these results is the investor
demand for emerging market debt that is skewed towards index-eligible bonds. We show
that an increase in the demand for emerging market debt, as proxied by investor flows to
emerging market debt funds, is highly positively correlated with the percentage of bond
issuances with face value of $500 million. Additionally, we show that funds that are less
familiar with emerging market corporate debt tend to have the highest demand for index-
eligible bonds. For instance, funds that specialize in developed market securities increased
their share of investments in emerging markets after the GFC. Also, using portfolio-level
data, we show that these funds tend to invest significantly more of their portfolio in bonds
with face value larger or equal than $500 million, relative to funds that have specialize in
emerging market securities.
Although the literature has emphasized the role of the monetary policy
environment in shifting the demand for emerging market securities, it is conceivable that
factors in emerging markets could also be contributing to aggregate changes in issuance
behavior. For example, changes in the willingness of emerging market firms to issue bonds
could reflect higher commodity prices that increase the profitability of investment
opportunities. The fact that we observe emerging market firms clustering their issuances
at exactly $500 million after 2008, however, strongly suggests the importance of bond
investor demand-side influences on the change in issuance behavior. It is highly unlikely
that new investment opportunities leading to greater needs for funds are clustered exactly
at issuance amounts of $500 million. Moreover, the fact that yield reductions are
discontinuous at the $500 million threshold is highly suggestive of bond investor demand-
side influences. Exogenous increases in firms’ desires for more funds in each capital raising
activity should lead to higher yields, not the lower ones we observe.
5
Next, we look at heterogeneous effects across the firm size distribution.
Specifically, we focus on two hypotheses. First, if the change in investor demand for bonds
is driving increased issuance, then the changes in behavior we document should be more
pronounced for large firms. The reason is that large firms exogenously face smaller costs
of issuing large amounts of debt. In particular, their scale of operations implies that they
are more likely to have immediate use for funds raised in the bond market.4 We find that,
in fact, large firms take greater advantage than small firms of the change in market
conditions after 2008. Second, we expect to find that relatively small firms are more likely
to see a change in the probability of issuing large-denomination debt when its cost
decreases. Whereas large firms might have been issuing large-denomination debt before
2008 simply by virtue of their more significant financing needs, small firms should have
been less likely to issue large-denomination debt prior to 2008. We find that small firms
do, indeed, see a larger increase in the probability of issuing large bonds after 2008. These
two findings are consistent with changes in investor appetite for large bonds, triggering
different responses by small and large firms.
To conclude the empirical analysis, we examine how firms use issuance proceeds,
distinguishing between the behavior of large and small firms. We show that emerging
market firms that issued dollar-denominated bonds in international markets with face value
equal to or greater than $500 million after 2008 have tended to hold more cash for every
dollar of debt issued than firms that issued lesser amounts. This result provides direct
evidence of the trade-off faced by firms when issuing large, index-eligible bonds: they can
secure lower financing costs, at the expense of hoarding cash. Moreover, the increased
holding of cash is greater for small firms than for large firms. This is consistent with small
4 In contrast, smaller firms responding to incentives from the investor side will likely have a harder time using large issuance proceeds, implying a cost that should make them less likely than large firms to take advantage of the changes in market conditions that favor large-denomination debt.
6
firms “stretching” to issue more debt than necessary to fund their investments in order to
take advantage of the size yield discount.
Our paper contributes to at least three different literatures. First, by showing that
bond index inclusion results in substantially lower yields and changes in issuance choices
by firms, we contribute to a large literature analyzing the effects of indexing on securities
prices and quantities. This literature has focused mostly on the effects of index rebalancing
on the pricing and liquidity of stocks and bonds.5 The evidence on the consequences of
index investing has been slim (Wurgler, 2011). Our main contribution is to show that the
use of indexes by institutional investors has important effects on firms’ financial decisions
and financing costs. Our evidence provides support for recent theoretical contributions
that seek to explain how the use of benchmarks enhances the liquidity of securities (Duffie
et al., 2017) and leads asset managers to effectively subsidize investments by benchmark
firms (Kashyap et al., 2018).6 Our paper extends to the global sphere the evidence that an
increase in demand from passive investors increases firms’ propensity to issue bonds in
the U.S. (Dathan and Davydenko, 2018).7
Second, we contribute to a growing literature studying how the low interest rate
environment after the GFC encouraged dollar-denominated corporate bond issuance
around the world at the expense of other forms of financing, such as bank borrowing.8
5 See, among others, Harris and Gurel (1986), Shleifer (1986), Chen et al. (2004), Barberis et al. (2005), Greenwood (2005), Hau et al. (2010), Claessens and Yafeh (2013), Chang et al. (2015), Raddatz et al. (2017), and Pandolfi and Williams (2019). 6 The magnitude of our estimates of the reduction in yields of index-eligible bonds is within the same range of the model-implied estimates provided by Kashyap et al. (2018). 7 Furthermore, firms in the United States responded to that demand by issuing a disproportionate number of bonds with sufficiently large size just to be eligible to be included in the most relevant indexes. We show that this size effect is present for emerging market debt issuers and that there is a large yield discount for issuing index-eligible bonds. We also show that the increased size-related yield discount for emerging market corporate debt had important consequences for the firm size distribution of corporate debt issuers and for cash holdings, especially by small firms. 8 See among others Adrian et al. (2013), Shin (2013), Becker and Ivashina (2014, 2015), Acharya et al. (2015), Carabin et al. (2015), Feyen et al. (2015), Du and Schreger (2016), Lo Duca et al. (2016), and Cortina et al. (2018) for analyses on the drivers of issuance in corporate debt markets. There has also been a closely related literature studying the behavior of bond funds and how they affect financial conditions for firms (Chui et al., 2014, 2016; Ramos and Garcia, 2015; Goldstein et al., 2017; Shek et al., 2017).
7
We show that the search for yield by institutional investors interacted with the institutional
arrangements determining index eligibility and the market structure for international debt
securities produced a rising incentive for emerging market firms to issue $500 million
bonds after the GFC. This has important consequences for costs and firms’ financing
decisions.
Third, our paper is related to the literature analyzing the influences on firms’
leverage and cash holdings choices, with particular emphasis on the increase in corporate
cash holdings.9 For example, Xiao (2018) argues that firms that substitute from bank
financing to bond financing increase their holdings of cash for precautionary savings. In
this paper we also find that the structure of the corporate bond market can create strong
incentives for “over borrowing” by small firms, which end up holding more cash than
needed for their investment projects.
The rest of the paper is organized as follows. Section 2 provides a theoretical
framework to understand how the search for yield can create a yield discount for index-
eligible debt, discussing the consequences for issuers. Section 3 describes our data sources.
Section 4 presents our issuance-level results. Section 5 examines bond holding differences
among mutual funds with respect to large bonds eligible for inclusion in indexes. Section
6 reports firm-level results that distinguish between the bond-issuance and cash-holding
behaviors of large and small firms. Section 7 concludes.
2. Theoretical Framework
Our theoretical discussion has three parts. First, we review the literature explaining why
inclusion in an index can increase the value of a security in the market. Second, we consider
why the advantages of inclusion in an index should vary over time for emerging market
9 See, for example, Bates et al. (2009), Falato et al. (2013), Begenau and Palazzo (2017), and Bruno and Shin (2017).
8
corporate debt. Third, we apply these theoretical principles to a simple model of index
inclusion in the emerging market corporate debt market, where issuance size thresholds
are the key determinant of index inclusion.
2.1. Why Does Index Inclusion Increase Corporate Debt Securities Prices?
Duffie et al. (2017) show that introducing a market benchmark improves price
transparency and promotes trade. Their paper explains how the existence of market
benchmarks – defined as “a measure of the ‘going price’ of a standardized asset at a
specified time” – mitigate search frictions, which are particularly relevant in over-the-
counter markets, such as markets for corporate debt. Although their study does not
consider the effects of a benchmark on different securities, by construction, the
information content of the benchmark should be greatest for those large securities that are
components of the benchmark. Thus, the benchmark index reduces search costs and
increases liquidity for the included securities that participants are willing to hold and trade.
Kashyap et al. (2018) study more directly how inclusion in an index produces a
higher price because asset managers – who are penalized by tracking error – face a strong
incentive to hold securities that are included in the benchmark, which they term the
“benchmark inclusion subsidy.” Furthermore, they show that the higher the risk of the
investment, the greater the benchmark inclusion subsidy: the pricing premium for
inclusion is an increasing function of the security’s riskiness.
In summary, irrespective of whether securities are traded directly by investors or
by intermediaries, securities that are included in benchmarks will tend to be more liquid
and will enjoy a price premium related to liquidity. The presence of institutional investors
who care about tracking error adds another pricing premium to securities that are included
in the index. This premium, which gives rise to the size yield discount that lowers firms’
cost of funds, is an increasing function of risk.
9
2.2. Why Does the Size Yield Discount Rise in Response to a Sudden Demand Increase?
We hypothesize that a surge in investor demand for high-yield dollar-denominated
emerging market debt results in a large increase in the proportion of bonds that are
managed by asset managers that have relatively little experience with investing in emerging
market corporate debt. Some of these managers might enter as new emerging market fund
specialists, and will be particularly interested in minimizing tracking error by purchasing
index-eligible corporate debt. Others, such as those managing broader portfolios, will find
it attractive to purchase index-eligible debt when “crossing over” into the emerging market
asset class because of its greater liquidity. The assets of funds investing in broader
portfolios tend to be large and managers value the ability to get in and out of positions,
especially those that are outside their primary mandate, without having a price impact.10
Three frictions in asset management can explain the increase in the fraction of the
newly issued debt that is managed by fund managers that lack experience in the emerging
market asset class. These are: a human-capital-scarcity friction, a relationship-value friction,
and a position-size-limit friction.11 The three frictions pertaining to fund managers,
combined with the potential conservatism of new investors, have a clear implication. When
low interest rates in developed economies produce a surge in demand for relatively risky
emerging market corporate debt, the incremental portfolio position in the new asset class
is likely to place more value on securities that are part of the index because of their greater
10 Emerging market securities, and especially corporate securities, are a highly specialized asset class. The risks that affect the value of these securities are often quite different from those affecting developed country sovereign or corporate debt (Beim and Calomiris, 2001; Kaminsky and Schmukler, 2008; Karolyi, 2015; Calomiris and Mamaysky, 2019). The risks include internal and external political and geopolitical events. As a response, a specialized group of mutual funds and hedge funds hire and train asset managers to manage portfolios of emerging market securities. This specialized group of managers are skilled at monitoring and managing the constellation of risks that are relevant to this asset class. 11 First, it is not possible to suddenly increase the supply of trained and experienced emerging market corporate debt asset managers (a human-capital-scarcity friction). Second, preexisting relationships between investors and fund managers tend to encourage investors to place money in the funds they invested in before, which limits the movement of funds to specialized emerging market funds (a relationship-value friction). Third, fund managers cannot manage an unlimited amount of funds effectively, and so preexisting fund managers who are experts in the emerging market corporate debt asset class might not be able to take on all the new demand, even if ultimate investors were willing to move funds to specialist managers (a position- size-limit friction).
10
liquidity and lower tracking error. For this reason, the price premium associated with index
inclusion should rise. We summarize this implication as:
Hypothesis 1: A sudden increase in demand for emerging market corporate debt should produce a
relative increase in the demand for bonds included in global indexes. This should result in an increase in
the price (i.e., reduction in the yield) of large, index-eligible debt.
The mechanism behind the reduction in the yield of index-eligible bonds relies on
an increase in the funds that are managed by managers who are less experienced in
emerging market corporate debt and tend to hold more index-eligible bonds. This leads
to the following corollary:
Cross-over Fund Corollary: After the surge in demand for emerging market corporate debt, “cross-
over” funds (those managing broader portfolios, such as global debt funds) with less experience in emerging
market corporate debt will hold a larger proportion of securities that are included in the index than
experienced emerging market corporate debt specialists.
2.3. Implications for Issuers: A Simple Model of Bond Issuance
Assume a continuum of emerging market firms that are potential bond issuers. Each firm
has an investment opportunity of a predetermined scale equal to 𝑋𝑋, where the cumulative
distribution function of 𝑋𝑋 is given by 𝐹𝐹(𝑋𝑋). 𝑋𝑋 represents the size of the firm in the model.
Each investment opportunity has the same gross return 𝑅𝑅 and has a positive net present
value. Firms finance their investment issuing bonds in foreign currency, so each firm will
issue at least the amount 𝑋𝑋. If firms issue more than 𝑋𝑋, they hold the difference between
the amount issued and 𝑋𝑋 as cash.
Assume there is a corporate debt index that includes only bonds of face value equal
to or greater than 500 (equivalent to $500 million in the data). We assume there is a yield
discount for index-eligible debt. The interest rate firms pay if they issue 𝑋𝑋 is equal to 𝑌𝑌 if
11
𝑋𝑋 < 500 and equal to 𝑌𝑌500 < 𝑌𝑌 if 𝑋𝑋 ≥ 500. We denote the size yield discount by 𝐷𝐷,
where 𝐷𝐷 = 𝑌𝑌 − 𝑌𝑌500.
Holding cash is costly because it earns a low return of 𝑌𝑌∗ < 𝑌𝑌 − 𝐷𝐷. Large firms
(𝑋𝑋 ≥ 500) do not have a choice to make; they simply issue a bond of size 𝑋𝑋 and enjoy
the lower financing cost. Small firms (𝑋𝑋 < 500), on the other hand, face a trade-off. They
can issue 𝑋𝑋 or “stretch”, which implies issuing 500 and holding the remaining (500 − 𝑋𝑋)
in cash. Given the cost of holding cash, firms with 𝑋𝑋 < 500, would never choose to issue
amounts of bonds between 𝑋𝑋 and 500.12 Profits under each alternative (issuing 𝑋𝑋 or issuing
500) are given by:
𝛱𝛱𝑋𝑋 = 𝑋𝑋𝑅𝑅 − 𝑋𝑋𝑌𝑌, (1)
𝛱𝛱500 = 𝑋𝑋𝑅𝑅 − 500(𝑌𝑌 − 𝐷𝐷) + (500 − 𝑋𝑋)𝑌𝑌∗. (2)
A firm will decide to issue 500 instead of 𝑋𝑋 if and only if 𝛱𝛱500 > 𝛱𝛱𝑋𝑋, which
implies:
𝑌𝑌𝑌𝑌 − 𝐷𝐷
+𝑌𝑌∗(500 − 𝑋𝑋)𝑋𝑋(𝑌𝑌 − 𝐷𝐷) >
500𝑋𝑋
. (3)
This inequality implies a critical value of 𝑋𝑋 above which small firms issue 500 in
debt:13
𝑋𝑋� =500(𝑌𝑌 − 𝐷𝐷 − 𝑌𝑌∗)
(𝑌𝑌 − 𝑌𝑌∗) . (4)
Let 𝐼𝐼 denote the optimal issuance size. Each firm’s optimal issuance size depends
on the size of the firm. Thus:
12 The profit of a firm with size 𝑋𝑋 < 500, issuing 𝑋𝑋, is 𝛱𝛱𝑋𝑋 = 𝑋𝑋𝑅𝑅 − 𝑋𝑋𝑌𝑌. If that firm issues 𝑋𝑋′ ∈ (𝑋𝑋, 500), it obtains profits equal to 𝛱𝛱𝑋𝑋′ = 𝑋𝑋𝑅𝑅 − 𝑋𝑋′𝑌𝑌 + (𝑋𝑋′ − 𝑋𝑋)𝑌𝑌∗. We can re-write those profits as: 𝛱𝛱𝑋𝑋′ = 𝑋𝑋𝑅𝑅 −𝑋𝑋𝑌𝑌 − 𝑋𝑋′𝑌𝑌 + (𝑋𝑋′ − 𝑋𝑋)𝑌𝑌∗ + 𝑋𝑋𝑌𝑌 = 𝛱𝛱𝑋𝑋 − (𝑋𝑋′ − 𝑋𝑋)(𝑌𝑌 − 𝑌𝑌∗). Given the opportunity cost of cash (𝑌𝑌∗ < 𝑌𝑌), we get that 𝛱𝛱𝑋𝑋′ < 𝛱𝛱𝑋𝑋 , so the firm will never choose to issue 𝑋𝑋′ ∈ (𝑋𝑋, 500). 13 Intuitively, the first two expressions in this inequality capture the benefits to issuing 500, namely the lower interest rate paid on debt and the additional revenues from interest on cash holdings, while the third term captures the higher debt service cost associated with a larger amount of debt.
12
𝐼𝐼 = �𝑋𝑋 𝑖𝑖𝑖𝑖 𝑋𝑋 < 𝑋𝑋�
500 𝑖𝑖𝑖𝑖 𝑋𝑋� ≤ 𝑋𝑋 < 500.𝑋𝑋 𝑖𝑖𝑖𝑖 𝑋𝑋 ≥ 500
(5)
Firms in the size interval �𝑋𝑋�, 500� stretch to issue 500. For these firms, the amount
they issue (I) is greater than the amount of their investment opportunity (X). For other
firms, the amount of bond issuance is equal to the size of their investment opportunity.
Let 𝐺𝐺(𝐼𝐼) denote the cumulative distribution function of issuance size (i.e., the percentage
of issuers that issue the amount 𝐼𝐼or less):
𝐺𝐺(𝐼𝐼) = �𝐹𝐹(𝐼𝐼) 𝑖𝑖𝑖𝑖 𝑋𝑋 < 𝑋𝑋�𝐹𝐹(𝑋𝑋�) 𝑖𝑖𝑖𝑖 𝑋𝑋� ≤ 𝑋𝑋 < 500𝐹𝐹(𝐼𝐼) 𝑖𝑖𝑖𝑖 𝑋𝑋 ≥ 500
. (6)
Figure 1, Panel A plots the cumulative distribution of issuance size. The cumulative
distribution is flat between �𝑋𝑋�, 500� because no firm issues in this size interval. There is
then a discrete a jump in the distribution at 500, driven by the mass of small firms that find
it optimal to stretch and issue 500.
We model an increase in demand for emerging market corporate debt as an
exogenous increase in the size yield discount 𝐷𝐷, in line with Hypothesis 1. Because 𝑋𝑋� is a
decreasing function of 𝐷𝐷, the increase in the size yield discount reduces the critical value
above which small firms issue 500. Intuitively, as the yield reduction benefit of issuing a
bond of 500 increases, firms become more attracted to issuing them. This leads to the
following hypothesis:
Hypothesis 2: A sudden increase in demand for emerging market corporate debt should result in an
increased propensity to issue debt that is included in the index.
We illustrate Hypothesis 2 in Figure 2, Panel B. The discrete jump of the
cumulative distribution at 500 becomes larger, as more small firms stretch and issue 500
increases with the increased size yield discount.
13
Note that 𝑋𝑋� is a decreasing function of 𝑌𝑌∗. The intuition is that a higher return on
cash makes the strategy of issuing a bond larger than 𝑋𝑋 and investing the remaining
(500 − 𝑋𝑋) in cash more attractive. This comparative static implication derived from
Equation (4) – stating that the critical value 𝑋𝑋� is lower for higher values of 𝑌𝑌∗ – is
summarized in the following hypothesis:
Hypothesis 3: A higher local interest rate should result in a higher propensity to issue large, index-
eligible debt.
The model also has several cross-sectional predictions. First, by construction, only
firms with scale above 𝑋𝑋� find it convenient to stretch and issue a 500 bond:
Hypothesis 4: Large firms are more likely to issue large amounts of debt that make them eligible for
inclusion in the index.
In addition, as explained in Hypothesis 2, because an increase in the demand for
bonds that are included in the index increases 𝐷𝐷 (reducing their yield), it also reduces 𝑋𝑋�. A
rise in 𝐷𝐷 makes some firms that previously had an investment size (𝑋𝑋) that was too small
to warrant an issuance of 500 to now choose to issue 500. We summarize this comparative
static result in Hypothesis 5:
Hypothesis 5: An increase in the benefit of being included in the emerging market corporate debt index
causes some small firms, which previously would not have issued a sufficient amount of debt to gain inclusion
in the index, to issue debt large enough to gain inclusion in the index.
Lastly, an increase in the size yield discount 𝐷𝐷 has no effect on the cash holdings
of large firms, defined as those that would issue 500 or more in debt irrespective of the
changes in the yield discount. In contrast, small firms that prior to the increase in 𝐷𝐷 would
have chosen to issue 𝑋𝑋 in debt, respond to the increase in 𝐷𝐷 by choosing to issue 500 in
debt, rather than 𝑋𝑋 < 500, holding cash equal to (500 – 𝑋𝑋). We summarize this result
in Hypothesis 6:
14
Hypothesis 6: Small firms that are prompted by increased demand for index inclusion to issue a large
amount of debt will increase their cash holdings.
3. Data
We use data from different sources. The data on bond issuances come from the Thomson
Reuters Security Data Corporation Platinum database (SDC Platinum). This database
contains transaction-level information on new issuances of corporate bonds by public and
private firms. From this database, we obtain the date a bond is issued, the face value of the
bond, and the yield to maturity at issuance. SDC Platinum also contains additional
information that we employ, including the rating of the firm at issuance, the country of the
firm, the industry of the firm, the market in which the bond is issued, the type of bond
(fixed or flexible coupon), the currency of the bond, whether the issuance is private or
public, and the maturity at issuance of the bond.
We focus on issuances of corporate bonds in U.S. dollars, which is a prerequisite
to being included in the bond indexes we analyze. We only study issuances that take place
in international markets, defined as a firm issuing a bond in a market that is different from
its country of origin. Additionally, we compare international dollar-denominated bonds
issued by emerging market firms with a sample of investment grade bonds issued by firms
from developed markets. In this way, we are able to compare yield and issuance outcomes
for firms that are inherently riskier (emerging market firms) with a control group of firms
that are considered relatively safe (investment grade developed market firms). This
comparison is relevant because we hypothesize that investors’ search for yield leads them
to increase their exposure to riskier firms around the world.14
14 In the Appendix, we provide additional results using jointly high yield developed market firm bonds and emerging market firm bonds.
15
We include firms from 68 developed and emerging economies for the period 2000-
2016. We use the nationality of the firm that is provided by SDC Platinum to classify firms
into developed and emerging markets (as listed in Appendix Table 1).15 We include both
financial and non-financial firms, because the market structure effects that we document
affect issuances by any type of firms. However, our results are robust to excluding financial
firms. Our sample includes 19,906 issuances from 4,965 firms.
We complement these data with additional information, mainly from three
different sources. We use injections/redemptions to emerging market debt funds from
Emerging Market Portfolio Research (EPFR) Global to gauge changes in investor interest
in emerging market debt. We use data from Morningstar Direct on the asset level portfolios
of mutual funds to understand the different types of investors holding emerging market
corporate debt. For the use-of-funds analysis, we merge the SDC data with Worldscope
data, which provide information on the financial statements of firms. Those data include
important information on firms’ assets, cash holdings, and sales (reported in balance
sheets, income statements, and cash flow statements). Worldscope data are available for
44% of the firms in the SDC database, resulting in a merged dataset of 2,190 firms.
4. Corporate Bond Issuances
4.1. New Findings on Yields and Issuance Behavior
As discussed in Section 2, we conjecture that part of the surge in investor interest in
emerging market corporate debt after the GFC reflected a change in the investor base. We
hypothesize that this compositional shift, together with the existence of the CEMBI
Narrow index, with a $500 million minimum cutoff, produced an increase in the interest
15 SDC Platinum contains a category that classifies the type of bond issued, which sometimes conflicts with our classification using the nationality of the issuer. If this category indicates that an emerging market firm issues the bond, we classify it as such regardless of the nationality of the firm provided by SDC. This only affects 300 observations, which is only 1.5% of our sample.
16
of international investors for large ($500 million and greater) emerging market corporate
bonds.
To study how the shifts in size-dependent investor interest affected market yields,
we begin with simple comparisons. In Figure 2, we plot the evolution of the yield to
maturity during 2000-2016 for bonds issued by emerging market corporates with face value
below $300 million, between $300 and $500 million, and equal or above $500 million. We
observe that yields for all issuance sizes declined after the GFC, but the effect is particularly
pronounced for $500 million bonds.
In Figure 3, Panel A, we aggregate within the pre- and post-crisis periods and
compare the average yield to maturity of bonds of different issuance size for the two time
periods. We observe that, on average, yield to maturity decreases with issuance size. More
importantly, consistent with Hypothesis 1, after 2008, we observe a sharp decline in the
yield when moving to issuance sizes of $500 million (a fall of 115 basis points). This decline
at the $500 million threshold is much more pronounced than that observed in the pre-
2008 period, suggesting that after 2008 there was an increase in bond investors’ demand
for bonds of issuance size greater than or equal to $500 million. There is also a decline in
the yield when moving to the $300 million threshold, consistent with the CEMBI Broad
having a minimum size requirement for inclusion of $300 million.16 However, compared
to the pre-2008 period, yields for $500 million emerging market corporate bonds declined
after 2008 by relatively more.17
16 The CEMBI has two different versions. The CEMBI Broad, which includes smaller securities and has a cutoff of $300 million and the CEMBI Narrow with an inclusion cutoff of $500 million. The latter index is composed of more liquid and selected securities. At the end of 2017, $61 billion tracked the CEMBI Broad, and $24 billion the CEMBI Narrow. While this could indicate a larger preference toward $300 million bonds, the assets tracking the EMBI (with a cutoff of $500 million) have been much larger than the assets tracking specifically corporate debt in emerging markets. For a more detailed account of the different indexes and requirements for inclusion, see Appendix 1 and Appendix Table 2. 17 Another notable feature in Figure 3, Panel A is the increase in yields from issuing $100 to $200 million in the post-2008 period. It is possible that firms that issued $200 million were constrained to do so because they could not stretch to issue $300 or $500 million. Firms that were unable to stretch in the post-2008 period might be indistinguishably riskier than firms that issued $200 million in the pre-2008 period, which could explain why yields for $200 million issuances remained higher in the post-2008 period.
17
Figure 3, Panel B presents the same analysis as Panel A for investment grade
corporate issuers in developed markets. Yields for issuances at the $500 million threshold
declined after 2008 by about 42 basis points. However, that decline was not much greater
than what is observed for the pre-2008 period, 15 basis points, suggesting a much larger
relative post-crisis effect on yields for emerging market firms.
Next, we study the implications of the reduction in yields of large, index-eligible
bonds on corporate bond issuance behavior. Figure 4 plots the evolution of the total value
of U.S. dollar-denominated corporate bonds issued by emerging market firms (Panel A)
and the evolution of the total number of issuances (Panel B). The figure shows that the
value of international bond issuances by emerging market firms increased sharply after
2008. Between 2008 and 2013, the value of those bond issuances increased by 380%.
Consistent with Hypothesis 2, Table 1 also shows that bonds above $500 million
represented only 33% of the total value of bonds issued between 2000 and 2008. After
2008, their share of the total nearly doubled to 62%. This is an important new finding:
after 2008, not only did total emerging market corporate bond issuances increased, there
was also a dramatic compositional shift from small issuances to large issuances ($500
million or more). Similarly, whereas the number of bonds issued above $500 million
represented 11% of the total number of bonds between 2000 and 2008, their share
increased to 33% after 2008, as illustrated in Table 1.
To study this compositional change in more detail, Figure 5, Panel A shows the
cumulative distribution of emerging corporate bond issuances by size. We plot the
distribution for the periods before and after 2008. Firms issue bonds of all sizes, ranging
from amounts less than 10 million to nearly a billion dollars. For the post-2008 period, we
observe a discrete jump in the distribution at $500 million, indicating a new discontinuity
in the distribution, with 18% of all bond issuances having a face value exactly equal to $500
million. This discontinuity was much more muted in the pre-2008 period. The empirical
18
cumulative distributions of issuance size resemble the model-based distributions plotted
in Figure 1.
The fact that, after 2008, we observe emerging market firms clustering their
issuances at exactly $500 million points to the importance of the investor side. That is, the
investor demand for bonds appears to have influenced the change in issuance behavior by
firms. We observe a smaller increase for issuances of $300 million after 2008, despite an
important decrease in yields in that threshold. One potential explanation is that, because
the benefit of reduced yield for issuing $500 million is much larger than for issuing $300
million, many firms decided to issue $500 million bonds rather than $300 million bonds.
Figure 5, Panel B replicates the previous figure, but for the sample of investment
grade firms issuing dollar-denominated bonds in developed economies. For those issuers,
we observe a smaller jump in the distribution at $500 million, and one that is more similar
before and after 2008. This is consistent with low-risk, advanced economy firms with lower
bond yields responding less to the post-2008 search-for-yield phenomenon. The difference
between corporates across the two types of countries suggests that changes in the investor
side during the post-GFC environment was much more relevant for emerging market
corporate bond issuers than for developed country investment grade issuers.18
Table 2 reports the statistical significance of the differences in means for yields and
issuances before and after 2008, for emerging economy issuers and investment grade
developed market issuers. Panel A shows that yields fell after 2008 for both bonds with
face value in the [400:500) range and those in the [500:600) range (expressed in millions of
U.S. dollars). But they fell much more for emerging market issuances in the [500:600)
18 Results for high-yield developed country issuers’ yields and issuances are very similar to those of our emerging market firms sample (Appendix Figure 2). These two sets of firms share two important characteristics. First, they are inherently riskier than investment grade developed market firms. Furthermore, these high-yield developed economy firms also can be included in special indexes that are similar to the CEMBI and EMBI. The Bloomberg Barclays High Yield Very Liquid Index is an important benchmark for these firms that only includes high yield dollar-denominated debt from developed market firms, with a minimum issue size of $500 million.
19
range. The triple difference test is statistically significant and shows a differential of almost
100 basis points in the decline in yields between emerging and developed markets. The
table shows analogous comparisons in the issuance activity (Panel B), which reacted
positively to the yield decrease, again especially in emerging markets in the [500:600)
range.19
4.2. Regression Analysis
We now consider more formally, in a regression format, how yields and issuances of bonds
of different size categories changed after 2008 for emerging market firms. The regressions
allow us to control for observable and unobservable characteristics that can predict yields
and issuance size. As before, we include both emerging market issuers and investment
grade developed market issuers in our analysis. We estimate the following regression for
bond yields:
𝑌𝑌𝑖𝑖𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖 = � �𝛽𝛽𝑋𝑋𝐷𝐷𝐷𝐷 ∗ 𝐷𝐷[𝑋𝑋:𝑋𝑋+100)𝑖𝑖𝑖𝑖 + 𝛽𝛽𝑋𝑋𝐷𝐷𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 ∗ 𝐷𝐷[𝑋𝑋:𝑋𝑋+100)𝑖𝑖𝑖𝑖
𝑋𝑋=100,200,…,900
∗ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝛽𝛽𝑋𝑋𝐸𝐸𝐷𝐷 ∗ 𝐷𝐷[𝑋𝑋:𝑋𝑋+100)𝑖𝑖𝑖𝑖 ∗ 𝐷𝐷𝐸𝐸𝐷𝐷 + 𝛽𝛽𝑋𝑋𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 ∗ 𝐷𝐷[𝑋𝑋:𝑋𝑋+100)𝑖𝑖𝑖𝑖
∗ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 ∗ 𝐷𝐷𝐸𝐸𝐷𝐷� + 𝜃𝜃𝑐𝑐 + 𝜃𝜃𝑗𝑗 + 𝜃𝜃𝑞𝑞𝑞𝑞 + 𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖.
(7)
In this specification, 𝑌𝑌𝑖𝑖𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖 is the yield to maturity of a bond issued by firm 𝑖𝑖 in
time 𝑃𝑃, where 𝑃𝑃 can be any given day during our sample period. 𝐷𝐷[𝑋𝑋:𝑋𝑋+100)𝑖𝑖𝑖𝑖 is a dummy
variable that indicates if the bond issued is of size [𝑋𝑋:𝑋𝑋 + 100), where 𝑋𝑋 =
100, 200, … ,900 million U.S. dollars.20 𝐷𝐷𝐸𝐸𝐷𝐷 is a dummy variable that indicates whether a
firm belongs to the emerging market category. 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 is a dummy variable that indicates if
a bond was issued in the post-2008 period. 𝜃𝜃𝑐𝑐 , 𝜃𝜃𝑗𝑗 , and 𝜃𝜃𝑞𝑞𝑞𝑞 are country, industry, and
19 Appendix Table 3 reports similar results using narrower bins. 20 Few firms issue bonds larger than $1,000, which makes the statistical comparison difficult, and thus we exclude these bonds.
20
quarter-year fixed effects. 𝑍𝑍𝑖𝑖𝑖𝑖 is a vector of bond controls, including whether the bond
coupon rate is fixed or flexible, whether a bond is issued in public or private markets,
whether the issuer is foreign owned, and a dummy variable indicating whether the firm is
government owned. The regressions also control for the maturity and rating of the bonds.
We cluster the standard errors in all regressions by country and quarter-year.
We are interested in the estimation of 𝛽𝛽𝑋𝑋𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖. These coefficients indicate how
the yield has changed in the post-2008 period relative to the pre-2008 period for a bond
of size [𝑋𝑋:𝑋𝑋 + 100) that was issued by an emerging market firm relative to one issued by
an investment grade developed market firm. More specifically, we estimate (controlling for
unobservables) the change in the size yield discount from the pre-2008 period to the post-
2008 period for emerging markets relative to developed markets. This differential is
captured by 𝛽𝛽500𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 − 𝛽𝛽400
𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖.
In our issuance regressions, we use the following specification:
𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑌𝑌[𝑋𝑋:𝑋𝑋+100)𝑖𝑖𝑖𝑖 = 𝜃𝜃𝑐𝑐 + 𝜃𝜃𝑗𝑗 + 𝜃𝜃𝑖𝑖 + 𝛽𝛽 ∗ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 ∗ 𝐷𝐷𝐸𝐸𝐷𝐷 + 𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖,𝑖𝑖, (8)
where the dependent variable is a dummy variable that indicates whether bond 𝑖𝑖, issued at
time 𝑃𝑃 is of size [𝑋𝑋:𝑋𝑋 + 100), where 𝑋𝑋 = 100, 200, … ,900.21
Equations (7) and (8) are effectively difference-in-difference specifications, where
we use developed economy, investment grade firms as a counterfactual for the behavior
of emerging market firms. We are interested in the coefficient of the interaction term,
𝛽𝛽𝑋𝑋𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 in Equation (7) and 𝛽𝛽 in Equation (8). They measure either the change in the
yield to maturity of a certain size or the change in the probability of issuing a bond of a
21 In Appendix Table 4, we show that our results are robust to adding firm fixed effects. The specification with firm fixed effects is more stringent than the regression with industry fixed effects, because it allows us to control for unobserved time-invariant firm characteristics. However, to identify firm fixed effects, we need to restrict our sample to firms that have issued at least two bonds, one in the pre-2008 period and another in the post-2008 period, forcing us to lose many observations.
21
certain size, before and after 2008, for emerging market firms relative to the same change
for developed economy firms.
To test for pre-treatment parallel trends, in Figure 6, Panel A displays the evolution
of the average yield to maturity over the period 2000 to 2016 for $500 million bond
issuances by emerging market issuers and developed market investment grade issuers,
respectively. Panel B shows the evolution of the average number of bond issuances of size
equal to $500 million, relative to the total number of issuances, for the same two sets of
issuers over the same period. Until 2008, we observe a similar pattern in yields and
issuances for the two groups. After 2008, we observe a sharp decline in the yields of $500
million issuances and an increase in the number of $500 million issuances only for
emerging market bond issuers.
We report the results of estimating Equation (7) in Table 3. To make the table
more readable, we report only the coefficients for 𝛽𝛽𝑋𝑋𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 in the table.22 We compare the
size yield discount for emerging market issuers after 2008 with the size yield discount for
developed economy investment grade issuers after 2008, taken relative to the pre-2008
values. We find that this triple differential, (𝛽𝛽500𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 − 𝛽𝛽400
𝐸𝐸𝐷𝐷,𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖), is 99 basis points, which
is statistically different from zero. This size yield discount difference falls to 92 basis points
when we add country, industry-year, and quarter-year fixed effects (Column 2). When we
add bond controls, maturity, and ratings fixed effects, the size yield discount difference
becomes 76 basis points and remains statistically different from zero (Column 3).
22 In Appendix Table 5 and 6, we report all the estimated coefficients. With those coefficients we can compute the reduction in yields from issuing $500 million bonds rather than $400 million bonds within a country group, between the post-2008 and pre-2008 periods. For developed market firms, this double differential ranges from 3 to 22 basis points, depending on the controls used, and is not statistically different from zero. This double differential for emerging market firms ranges from 93 to 218 basis points and is statistically different from zero.
22
With respect to issuance quantities, we estimate Equation (8) using the issuance
indicator for bonds in different size bins as the dependent variable.23 Table 4, Panel A
shows that the coefficient of the interaction term is positive and statistically significant for
issuances of size between $500 and $600 million. This means that after 2008 emerging
market bond issuers were 9 percentage points more likely to issue bonds in this size bin,
relative to developed economy investment grade issuers. This is a sizable effect, especially
when compared to the average probability of an emerging market issuer issuing a bond of
$500 million before 2008, which is 10%. At the same time, the likelihood of issuing bonds
in the [100:200) bin decreased, as emerging market issuers substituted large bond issues
for small ones. The issuance of $300 to $400 million bonds also increased after 2008, but
by less than for the $500, consistent with our finding that the decrease in the yields for this
size group was smaller. As before, results are very similar when we control for bond
characteristics (Panel B).24,25
4.3. Placebo Test of Bond Index Inclusion
To provide a placebo test of whether our results are driven by the index inclusion
requirements, we re-estimate Equations (7) and (8) using bonds that are not included in
the CEMBI index because of other index-inclusion requirements unrelated to size.
Specifically, we keep only floating rate bonds and bonds with less than five years of
maturity. Because these bonds are not included in the index, irrespective of size, we expect
to find no effects on issuances and yields at the $500 million threshold. Table 3 (Column
23 Results are very similar when we use spreads over the maturity-relevant U.S. treasuries, rather than yields as the dependent variable. In additional robustness tests we also include maturity-time and ratings-time fixed effects and results remain very similar (Appendix Table 7). 24 We also test whether the treatment effect of index inclusion interacts with the Treasury basis variable constructed by Jiang et al. (2018, 2019), which they interpret as a convenience yield for U.S. Treasuries. Most of the variation in that variable occurs during the 2007-2009 crisis. We find that there is no evidence of an interaction after the crisis (Appendix Table 8). 25 Table 4 estimates Equation (6) for a sample of strictly positive issuance observations. In Appendix Table 9, we re-estimate the equation for a sample containing all observations (including those with no issuances) and the results remain unchanged.
23
4) reports the results of this exercise for yields and Table 5 reports the results for issuances.
Indeed, we observe no significant change in the yields of bonds of size between $500 and
$600 million and no significant increase in issuances for these bonds. This test supports
the hypothesis that the decrease in yields and the increase in issuances after 2008 for bonds
of size between $500 and $600 million reflect the effect of index inclusion, not size per se.
4.4. Carry Trade Influences
Our theoretical framework in Section 2 also predicts that, ceteris paribus, firms should be
more likely to issue $500 million bonds when they are located in countries where there is
a relatively large expected local interest rate from investing in cash (Hypothesis 3). In Table
6, we test that prediction by exploiting the cross-country variation in our sample. We
regress a dummy that is one if a firm issued a $500 million bond and zero if the firm issued
any bond below that size on our carry trade variable. Following Bruno and Shin (2017),
our measure of carry takes the form of a “carry Sharpe ratio,” which is the difference
between the local money market interest rate and the U.S. money market interest rate. We
adjust for exchange rate risk by dividing the interest rate differential by the annualized
volatility of the exchange rate during the previous two quarters. Like a Sharpe ratio, this
measure captures the expected profit from investing in local currency adjusted by exchange
rate risk. We include time fixed effects to exploit the cross-country variation, along with
different sets of fixed effects and bond controls. We find that there is a positive and
statistically significant association between the carry trade measure and the probability of
issuing $500 million bonds. Interestingly, in results not reported here, we find no
statistically significant carry effect when we do not adjust for the volatility of the exchange
rate. This suggests that firms do take the risk of exchange rate depreciation into account
when deciding to issue dollar-denominated bonds.
24
5. Inspecting the Mechanism: The Role of Institutional Investors
We posit that the driver of change in the importance of index eligibility over time is the
movement to a low interest rate environment in developed economies. The search for
yield across the world and the increase in investor interest in emerging market corporates
raised the value to fund investors of holding large emerging market bonds that are part of
indexes.
We also conjecture that the composition of international investors changed from
a near exclusive reliance on a preexisting group of specialist emerging market corporate
bond investors toward a broader investor base. The latter includes old and new emerging
market sovereign bond funds and developed economy corporate bond funds, managed by
people with relatively little prior experience in the emerging market corporate asset class.
We label these developed market institutional investors and emerging market sovereign
investors the “cross-over investors,” because they are crossing over from other asset
classes into the emerging market corporate debt asset class. In the cross-over corollary in
Section 2, we hypothesize that these cross-over investors will tend to invest more in index-
eligible bonds relative to specialists.
Here, we explicitly test the cross-over corollary, using data on different funds’
holdings of emerging market corporate bonds. Figure 7 presents two pieces of evidence
that connect investor interest with changes in the composition of emerging market
corporate bond issuance. Panel A plots the cumulative flows into mutual funds that invest
in emerging market sovereign and corporate debt from 2003 to 2016. It also plots the
number of $500 million bonds issued by emerging market firms, as a fraction of all bonds
issued by these firms. The correlation between the two is very high (0.93), showing a clear
connection between the growing investor interest in emerging market debt and the
growing relative importance of issuances that just meet the threshold of $500 million. Panel
B plots the percentage of the portfolio invested in emerging markets by developed market
25
debt mutual funds from 2005 to 2016. We observe a sharp increase in the weight dedicated
to emerging market securities within these funds, which is consistent with a crossing-over
of developed market debt funds into emerging market securities. Together, these figures
show the increase of investor interest in emerging market debt securities, from ultimate
investors and from asset managers from developed countries.
We complement the evidence in Figure 7 with additional evidence showing that
investors less familiar with the emerging market corporate asset class tend to hold a greater
proportion of index-eligible emerging market corporate debt. We assemble data from
Morningstar Direct on debt funds that we categorize into emerging market corporate
specialists and cross-overs, using fund categories provided by Morningstar. Within the
cross-over category, we also classify funds into emerging market mixed, emerging market
sovereign, and developed markets.26 Our data contain 1,435 funds with $4,561 billion in
assets under management (Table 7).
Funds that specialize in emerging market corporate debt are relatively small
compared to funds that specialize in sovereign emerging market debt or developed market
funds. For each of the funds, we observe its portfolio at the end of December 2016. Most
of the funds in each category have at least one emerging market corporate bond in their
portfolio. Within each category, emerging market corporate debt constituted 2%, 16%,
18%, and 64% of the debt portfolios of developed market, emerging market sovereign,
emerging market mixed, and emerging market corporate specialists funds, respectively.
One more noteworthy feature is the importance of each type of fund in terms of
their investments in the dollar-denominated emerging market international corporate debt
market.27 Cross-over funds together invested $128.4 billion in emerging market corporate
26 Emerging mixed funds are those that invest in both emerging market corporate debt and sovereign debt (Appendix 2). 27 Most of the funds in our sample invested only in dollar-denominated emerging market corporate bonds issued in international markets. In 2016, these bonds represented 85% of their holdings in emerging market corporate bonds.
26
bonds, while emerging market corporate specialists invested $20.8 billion in these securities
at the end of 2016 (Column 7). Even though advanced market funds held a low fraction
of emerging market securities in their portfolios, the fact that the sizes of those funds tend
to be so large implies that they hold a substantial dollar amount in emerging market debt.
These data show the importance of cross-over investors for this market.
We test the cross-over corollary in Table 8. For each type of fund, we first compute
the total amount of U.S. dollar-denominated corporate emerging market bonds (issued in
international markets) held in the portfolio. Then, we compute the percentage of that
amount held in each of the following three categories: bonds with face value less than $300
million, bonds with face value in the $300-500 million range and bonds with face value
greater than or equal to $500 million. We compute the average percentage held in each
specific bucket size by each mutual fund category. We compare across funds of different
categories, and with respect to the outstanding amount of corporate bonds issued by
emerging market firms.
The results lend support to the cross-over corollary. Cross-over funds invest
relatively more in bonds with face value greater than or equal to $500 million. In fact, we
obtain a consistent fund pecking order with 83%, 78%, 74%, and 69% invested in this
bucket size, by developed market, emerging market sovereign, emerging market mixed,
and emerging market corporate specialists funds, respectively. We conduct a difference in
means test in Column 4 of Table 8 for each type of cross-over fund, relative to the
corporate emerging market funds. We find that sovereign emerging market and advanced
market funds display statistically significant differences with respect to the holdings of
corporate emerging market bonds. Additionally, we compare the portfolio of each type of
fund with the total amount outstanding of dollar-denominated international corporate
emerging market bonds at the end of 2016 (Column 5). In general, corporate emerging
27
market funds held a portfolio similar to the outstanding amount of corporate bonds,
whereas cross-over funds skewed their portfolio toward large bonds.
6. Consequences for Firms
Our analysis of yields and issuances in Section 4 is highly suggestive that a shift in bond
investor demand (search for yield) has been the main driver of the post-2008 yield decline
and issuance increase for large emerging market corporate bonds. However, that evidence
does not rule out some potential influences from issuers investment opportunities – that
is, greater growth opportunities or lower fundamental risks in emerging markets – in
driving some of the growth in large-face value emerging market corporate bond issuances.
In this section, we consider how firm-level differences could affect issuance
behavior. This analysis provides additional evidence that sheds more light on the role of
bond investor demand changes in driving our results. The evidence is reported in two
parts. First, we test the two implications about bond demand shifts for cross-sectional
differences in issuer responses (Hypotheses 4 and 5), both of which follow from the fact
that small firms face higher economic costs when issuing large amounts in the bond
market. Second, we examine the uses of funds raised by firms in the bond market
(Hypothesis 6) as part of our firm-level analysis.
6.1. Bond Issuance Differences by Small and Large Firms
Figure 8 tests a firm-size related implication of the post-GFC investor demand-side shift:
small-size firms should display the biggest change in their propensity to issue large, index-
eligible bonds (Hypothesis 5). Prior to 2008, small firms should have been very unlikely to
issue large amounts of debt, but some of them (those willing to accumulate excess cash
balances to access low-interest funding) decided to stretch and issue $500 million bonds
28
for the first time. Figure 8 is consistent with this prediction: the size distribution of firms
issuing bonds of $500 million or more shifted to the left after 2008.
In addition, we conduct Probit and Logit estimations, separately for emerging
market issuers and developed market investment grade issuers, to estimate how firm size
affects the change in the probability of issuing a large bond (greater or equal than $500
million) after the GFC. We estimate:
𝐷𝐷𝑖𝑖,𝑖𝑖 = 𝛽𝛽1𝑃𝑃𝑃𝑃𝑌𝑌 + 𝛽𝛽2𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝛽𝛽3(𝑆𝑆𝑖𝑖𝑆𝑆𝑌𝑌𝑖𝑖𝑖𝑖 ∗ 𝑃𝑃𝑃𝑃𝑌𝑌) + 𝛽𝛽4(𝑆𝑆𝑖𝑖𝑆𝑆𝑌𝑌𝑖𝑖𝑖𝑖 ∗ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃)+𝜀𝜀𝑖𝑖,𝑖𝑖, (9)
where 𝐷𝐷𝑖𝑖,𝑖𝑖 is a dummy variable equal to one if a firm issued a bond larger or equal than
$500, and zero if it issued a bond of smaller size. We measure the size of a firm with the
log of total assets.
The likelihood that a firm issued a bond of a given size in the pre- or post-crisis
period is 𝛽𝛽1 + 𝛽𝛽3 ∗ 𝑆𝑆𝑖𝑖𝑆𝑆𝑌𝑌 and 𝛽𝛽2 + 𝛽𝛽4 ∗ 𝑆𝑆𝑖𝑖𝑆𝑆𝑌𝑌, respectively. Table 9 shows that both
interaction terms (𝛽𝛽3 and 𝛽𝛽4) are positive and highly significant. This indicates that larger
firms were more likely to issue larger bonds than smaller firms, both before and after the
GFC. This is consistent with Hypothesis 4. Moreover, the change in the likelihood of
issuing a large bond after the GFC is given by: (𝛽𝛽2 − 𝛽𝛽1) + (𝛽𝛽4 − 𝛽𝛽3) ∗ 𝑆𝑆𝑖𝑖𝑆𝑆𝑌𝑌. If 𝛽𝛽4 < 𝛽𝛽3,
then the change in the probability of issuing a large bond in the post-2008 period is greater
for smaller firms than for larger firms. That is exactly what we find in Column 1
(0.420<0.501), which provides evidence consistent with Hypothesis 5. The changes are
larger in emerging markets than in developed economies.
These results are consistent the view that a shift in bond investor demand for
index-eligible debt acted as a treatment effect on emerging market bond issuers. Large
firms were exogenously positioned, by virtue of their size, to better take advantage of the
new issuance opportunities, which required firms to issue bonds of large size. Some small
firms in emerging markets, seeking to borrow at unusually low rates available in the post-
2008 environment to firms issuing large (index-eligible) bonds, stretched and engaged in
29
unprecedented issuance of large bonds, which resulted in a relatively large increase in the
probability of large bond issuance by those smaller firms.
6.2. Uses of Funds from Bond Issuances of Small and Large Firms
Lastly, we investigate the uses of funds by emerging market bond issuers, and differences
in the uses of funds by small and large firms. Firms taking advantage of the yield discount
in $500 million bonds might be issuing bonds that are larger than the investment project
opportunities they face. As a consequence, some issuing firms might devote a larger share
of the money raised in these issuances towards cash and short-term investments. To study
this, we follow the methodology by Kim and Weisbach (2008) and Erel et al. (2012). We
focus exclusively on the use of funds of cash and short-term investments accumulation.
We begin by calculating the accumulation of cash two years after each firm’s bond
issuance by estimating the following regression:
𝐶𝐶𝐼𝐼𝑃𝑃ℎ𝑖𝑖𝑐𝑐𝑖𝑖 = 𝛼𝛼𝑐𝑐 + 𝛼𝛼𝑖𝑖 + 𝛽𝛽𝑌𝑌𝑃𝑃𝛽𝛽 �1 + �𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑌𝑌𝐴𝐴𝑃𝑃𝑃𝑃𝑌𝑌𝑃𝑃𝑃𝑃
�𝑖𝑖𝑐𝑐𝑖𝑖�
+ 𝛾𝛾𝑌𝑌𝑃𝑃𝛽𝛽 �1 + �𝑂𝑂𝑃𝑃ℎ𝑌𝑌𝑃𝑃 𝑆𝑆𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑌𝑌𝑃𝑃
𝐴𝐴𝑃𝑃𝑃𝑃𝑌𝑌𝑃𝑃𝑃𝑃�𝑖𝑖𝑐𝑐𝑖𝑖� + 𝑍𝑍𝑖𝑖𝑐𝑐𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑐𝑐𝑖𝑖 ,
(10)
where 𝐶𝐶𝐼𝐼𝑃𝑃ℎ = log �𝑉𝑉𝑛𝑛−𝑉𝑉0𝐴𝐴𝑃𝑃𝑃𝑃𝐴𝐴𝑖𝑖𝑃𝑃
+ 1�. 𝑉𝑉 stands for cash holdings and short-term investments.
𝐼𝐼 = 2 denotes the two-year time period considered for the analysis.28 𝐴𝐴𝑃𝑃𝑃𝑃𝑌𝑌𝑃𝑃𝑃𝑃 are the total
assets of the firm in the year previous to the issuance. 𝑂𝑂𝑃𝑃ℎ𝑌𝑌𝑃𝑃 𝑆𝑆𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑌𝑌𝑃𝑃 =
𝑌𝑌𝑃𝑃𝛽𝛽 �∑ 𝑇𝑇𝑃𝑃𝑖𝑖𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑠𝑠𝑠𝑠𝑐𝑐𝐴𝐴𝑃𝑃𝑖𝑖−𝐼𝐼𝑃𝑃𝑃𝑃𝑠𝑠𝑇𝑇𝐼𝐼𝑐𝑐𝐴𝐴𝑛𝑛𝑖𝑖−1
𝐴𝐴𝑃𝑃𝑃𝑃𝐴𝐴𝑖𝑖𝑃𝑃+ 1�, where total sources of funds represent the total
28 Results for one year after the debt issue are similar to those reported for the two-year horizon, but the coefficients for the one-year horizon are larger for both small and large firms. Using the second year mitigates the heterogeneity across firms related to the reporting dates of financial statements (given that offering dates occur at different times in the offering year). In addition, firms might take some time to spend the cash raised in their issuances, so cash holdings in the first year might not be too informative. Therefore, we confine our analysis to the two-year horizon.
30
funds generated by the firm internally and externally during a given year. 𝑍𝑍𝑖𝑖𝑐𝑐𝑖𝑖 are firm
observable characteristics that we use as controls.
Figure 9, Panel A reports the results of estimating Equation (7) for the change in
cash and short-term investments as dependent variable, controlling for the log of initial
assets in the year before issuance, growth of sales, and the standard deviation of growth of
sales.29 We report the dollar effects, breaking down our sample into different categories.30
We find that emerging market firms issuing $500 million and above bonds tended to hold
more cash after a bond issuance in post-2008 period relative to the pre-2008 period.
Quantitatively, in the pre-2008, for every million-dollar raised, they held 0.12 million
dollars in cash and short-term instruments two years after the issuance. The estimate for
the post-2008 period jumps to 0.71 million dollars. We note that Equation (10) is estimated
with relatively few observations, which implies that the true increase may have been less,
given that the coefficients are not estimated very precisely. We do not observe this increase
in the use of cash and short-term instruments for emerging market firms issuing bonds
smaller than $500 million. Firms that issue these bonds held 0.41 (0.25) million dollars per
million dollars issued in before (after) 2008. We do not observe a similar increase for
developed market firms (whose estimates decline from 0.49 to 0.34).
If smaller firms were the ones stretching to take advantage of the yield discount in
$500 million bonds in the post-2008 period, then we should observe that these are the
firms driving our results in the uses of funds, and specifically the accumulation of cash. In
Figure 9, Panel B, we present the Kim and Weisbach (2008) analysis for the post-2008
29 It is conceivable that these results might be driven by selection bias. Emerging market firms that issue in the pre-2008 period differ on average from those issuing in the post-2008 period. There are several observable characteristics of firms that might be correlated with holdings of cash, such as the size of firms, their growth, and their uncertainty. We control for this possibility by adding these observables to the estimations. 30 One potential concern is that firms might issue bonds of different sizes during a given year. However, firms issue these types of bonds infrequently. The average emerging market firm only issues bonds of this type once every 6.6 years (Appendix Table 10).
31
period for emerging market firms, dividing companies into high- and low-asset firms
(above and below the country median of assets, respectively). Small firms tend to hold
much more cash than large firms after a bond issuance during this period, consistent with
our prediction.
7. Conclusions
The GFC led to a persistent period of low interest rates throughout the developed world.
This low interest rate environment produced a search for yield by institutional investors
that favored some classes of global securities, such as emerging market corporate debt,
that had not been as popular among developed countries’ institutional investors prior to
the crisis. In this paper, we show that institutional investors searching for yield in emerging
market corporate debt after 2008 strongly favored corporate debt securities that were large
enough to qualify for inclusion in market indexes.
Inclusion in market indexes is likely favored by institutional investors because
holding a portfolio of bonds included in the index improves the liquidity of their positions.
Some funds also benefit from holding bonds in the index because doing so reduces the
risk that their performance will deviate from the market benchmark. The benefits of index
inclusion are especially attractive for cross-over fund investors, which manage a
considerable pool of assets, lack experience with emerging market corporate debt, and
favor liquidity. Indeed, we find that cross-over funds hold especially significant
proportions of large, index-eligible emerging market corporate debt.
The sudden rise in the demand for emerging market corporate debt by fund
investors produced a sizeable increase in the yield discount associated with index eligibility,
and a large increase in the proportion of issuance of large, index-eligible corporate debt.
The financial rewards of issuing index-eligible debt after 2008 were significant. Firms able
to issue a $500 million bond, rather than, say, a $400 million bond, saved close to a full
32
percentage point in yield to maturity. These changes in issuance size were not apparent for
investment grade developed country corporate bond issues, which by virtue of their lower
preexisting risk and greater ability to attract institutional investors in the pre-2008 era were
less affected by the search for yield after 2008.
Large size emerging economy firms were exogenously better positioned to take
advantage of the new opportunities to issuing large bonds at lower yields. Smaller size
emerging economy firms, however, saw the greatest change in the probability of issuing
large bonds. The smaller issuers who stretched and issued large bonds were willing to retain
significant amounts of cash from the proceeds of their bond issues to access funds at a
lower cost.
Our findings raise important questions for future research. First, because the
increased discount on emerging market corporate debt was larger for risky debt, it might
have constituted a subsidy for greater risk taking. Did firms respond to this subsidy by
increasing the riskiness of their operations? Second, with respect to the extra cash holdings
of small firms issuing large bonds after 2008, how did the combination of dollar-
denominated debt and domestic cash holdings affect their exposure to exchange rate risk,
and their other risk-management practices? Also, if equity capital is scarce, did the
combination of increased leverage and additional cash from bond issuance by small firms
that stretched to raise their issuance amount crowd in or crowd out productive
investments? Third, in our empirical analysis our data did not permit us to distinguish
between the two alternative drivers of the yield discount for index eligibility (greater
liquidity or reduced tracking error). If liquidity is relatively important, then one would
expect that fund demand for index-eligible debt should be greater for debt with lower bid-
ask spreads. If tracking error is relatively important, then even relatively illiquid debt in the
index would enjoy substantial yield discounts in the primary market. Furthermore, tracking
error should be relatively unimportant for funds that do not track the CEMBI index.
33
References
Acharya, V., S. Cecchetti, J. de Gregorio, S. Kalemli-Ozcan, P. Lane, and U. Panizza (2015). “Corporate Debt in Emerging Economies: A Threat to Financial Stability?” Brookings Institution, Committee on International Economic Policy and Reform.
Adrian, T., P. Colla, and H.S. Shin, (2013). “Which Financial Frictions? Parsing the Evidence from the Financial Crisis of 2007 to 2009.” In NBER Macroeconomics Annual 2012, Vol. 27, edited by D. Acemoglu, J. Parker, and M. Woodford, University of Chicago Press, 159-214.
Barberis, N., A. Shleifer, and J. Wurgler (2005). “Comovement.” Journal of Financial Economics 75(2), 283-317.
Bates, T., K.M. Kahle, and R.M. Stulz (2009). “Why Do U.S. Firms Hold So Much More Cash than They Used To?”. Journal of Finance, 64(5), 1985-2021.
Becker, B. and V. Ivashina (2014). “Cyclicality of Credit Supply: Firm-Level Evidence.” Journal of Monetary Economics 62(C), 76-93.
Becker, B. and V. Ivashina (2015). “Reaching for Yield in the Bond Market.” Journal of Finance 70(5), 1863-1902.
Begenau, J., and B. Palazzo (2017). “Firm Selection and Corporate Cash Holdings.” National Bureau of Economic Research, Working Paper 23249.
Beim, D. O., and C.W. Calomiris (2001). “Emerging Financial Markets.” McGraw-Hill/Irwin, 2001.
Bruno, V. and H.S. Shin (2017). “Global Dollar Credit and Carry Trades: A Firm-level Analysis.” Review of Financial Studies 30(3), 703-749.
Calomiris, C.W. and H. Mamaysky (2019). “How News and Its Context Drive Risk and Returns Around the World.” Journal of Financial Economics, forthcoming.
Carabin, M., A. de la Garza, and O. Moreno (2015). “Global Liquidity and Corporate Financing in Mexico.” Bank of Mexico, Mimeo.
Chang, Y.C., H.G. Hong, and I. Liskovich (2015). “Regression Discontinuity and the Price Effects of Stock Market Indexing.” Review of Financial Studies 28(1), 212-246.
Chen, H., G. Noronha, and V. Singal (2004). “The Price Response to S&P 500 Index Additions and Deletions: Evidence of Asymmetry and a New Explanation.” Journal of Finance 59(4), 1901-1930.
Chui, M., I. Fender, and V. Sushko (2014). “Risks Related to EME Corporate Balance Sheets: The Role of Leverage and Currency Mismatch.” BIS Quarterly Review, September 2014, 35-47.
Chui, M., E. Kuruc, and P. Turner (2016). “A New Dimension to Currency Mismatches in the Emerging Markets-Non-Financial Companies.” Bank of International Settlements, Working Paper 550.
Claessens, S. and Y. Yafeh (2013). “Comovement of Newly Added Stocks with National Market Indices: Evidence from Around the World.” Review of Finance 17(1), 203-227.
Converse, N., E. Levy-Yeyati, and T. Williams (2018). “How ETFs Amplify the Global Financial Cycle in Emerging Markets.” Institute of International Economic Policy, Working Paper 2018-1.
Cortina, J., T. Didier, and S. Schmukler (2018). “Corporate Borrowing and Debt Maturity: The Effects of Market Access and Crises.” CEPR Discussion Paper DP13008 and Mo.Fi.R. Working Paper 149.
Cremers, M. and A. Petajisto (2009). “How Active Is Your Fund Manager? A New Measure that Predicts Performance.” Review of Financial Studies, 22(9), 3329-3365.
Cremers, M., M. Ferreira, P. Matos, and L. Starks (2016). “Indexing and Active Fund Management: International Evidence.” Journal of Financial Economics, 120(3), 539-560.
Dathan, M. and S.A. Davydenko (2018). “Debt Issuance in the Era of Passive Investment.” University of Toronto, Mimeo.
34
Du, W. and J. Schreger (2016). “Local Currency Sovereign Risk.” Journal of Finance 71(3), 1027-1070.
Duffie, D., P. Dworczak, and H. Zhu (2017). “Benchmarks in Search Markets.” Journal of Finance, 72(5), 1983-2044.
Erel, I., B. Julio, W. Kim, and M. Weisbach (2012). “Macroeconomic Conditions and Capital Raising.” Review of Financial Studies, 25(2), 341-376.
Falato, A., D. Kadyrzhanova, J. Sim, and R. Steri (2013). “Rising Intangible Capital, Shrinking Debt Capacity, and the US Corporate Savings Glut.” Board of Governors of the Federal Reserve System, Finance and Economics Discussion Series 2013-67.
Feyen, E., K. Gosh, K. Kibuuka, and S. Farazi (2015). “Global Liquidity and External Bond Issuance in Emerging Economies and Developing Economies.” World Bank, Research Working Paper 7363.
Goldstein, I., H. Jiang, and D.T. Ng (2017). “Investor Flows and Fragility in Corporate Bond Funds.” Journal of Financial Economics 126(3), 592-613.
Greenwood, R. (2005). “Short- and Long-Term Demand Curves for Stocks: Theory and Evidence on the Dynamics of Arbitrage.” Journal of Financial Economics 75(3), 607-649.
Harris, L. E. and E. Gurel (1986). “Price and Volume Effects Associated with Changes in the S&P 500 List: New Evidence for the Existence of Price Pressures.” Journal of Finance 41(4), 815-829.
Hau, H., M. Massa, and J. Peress (2010). “Do Demand Curves for Currencies Slope Down? Evidence from the MSCI Global Index Change.” Review of Financial Studies 23(4), 1681-1717.
Jiang, Z., A. Krishnamurthy, and H. Lustig (2018). “Foreign Safe Asset Demand and The Dollar Exchange Rate.” National Bureau of Economic Research, Working Paper 24439.
Jiang, Z., A. Krishnamurthy, and H. Lustig (2019). “Dollar Safety and The Global Financial Cycle.” Graduate School of Stanford Business, Mimeo.
Kaminsky, G., and S. Schmukler (2008). “Short-Run Pain, Long-Run Gain: Financial Liberalization and Stock Market Cycles.” Review of Finance, 12(2), 253-292.
Karolyi, G.A. (2015). Cracking the Emerging Markets Enigma. Oxford University Press, 2015. Kashyap, A.K., N. Kovrijnykh, J. Li, and A. Pavalova (2018). “The Benchmark Inclusion
Subsidy.” National Bureau of Economic Research, Working Paper 25337. Kim, W. and M. Weisbach (2008). “Motivations for Public Equity Offers.” Journal of
Financial Economics 87(2), 281-307. Lo Duca, M, Nicoletti, G., and A. Vidal Martinez (2016). “Global Corporate Bond
Issuance: What Role for US Quantitative Easing?” Journal of International Money and Finance, 60, 114-150.
Pandolfi, L. and T. Williams (2019). “Capital Flows and Sovereign Debt Markets: Evidence from Index Rebalancings.” Journal of Financial Economics, forthcoming.
Raddatz, C., S. Schmukler, and T. Williams (2017). “International Asset Allocations and Capital Flows: The Benchmark Effect.” Journal of International Economics, 108, 413-430.
Ramos, M. and S. Garcia (2015). “Is Trouble Brewing for EMEs?” Bank of Mexico, Working Paper 2015-08.
Shek, J., I. Shim, and H.S. Shin (2017). “Investor Redemptions and Fund Manager Discretionary Sales of EME Bonds: How Are They Related?” Review of Finance 22(1), 207-241.
Shin, H. S. (2013). “The Second Phase of Global Liquidity and its Impact on Emerging Economies.” Keynote Address at Federal Reserve Bank of San Francisco, Asia Economic Policy Conference.
35
Shleifer, A. (1986). “Do Demand Curves for Stocks Slope Down?” Journal of Finance 41(3), 579-590.
Wurgler, J. (2011). “On the Economic Consequences of Index-Linked Investing.” In Challenges to Business in the Twenty-First Century: The Way Forward, edited by G. Rosenfeld, J. Lorsch, and R. Khurana, American Academy of Arts and Sciences, 20-34.
Xiao, J. (2018). “Corporate Debt Structure, Precautionary Savings, and Investment Dynamics.” Meeting Papers 887, Society for Economic Dynamics.
36
Appendix 1. The Emerging Market Debt Index Universe
There are relatively few indexes that track emerging market corporate debt denominated
in foreign currencies. The most prominent index provider companies that cater to
investors interested in emerging market debt are Barclays/Bloomberg, Citigroup, and J.P.
Morgan. Among them, J.P. Morgan is arguably the leader in the emerging market segment
in terms of the funds that track their performance against its indexes. For instance, as of
July 2017, EPFR Global tracks the performance of 450 specialized emerging market debt
funds. Of those, 394 funds (88%) declared to be tracking their performance against a J.P.
Morgan index. These funds had $317 billion under management, and $280 billion (88%)
of those assets are benchmarked against J.P. Morgan indexes.
Throughout the paper we focus on the important J.P. Morgan bond indexes. There
are three broad families of J.P. Morgan emerging market indexes: the CEMBI (corporate
debt denominated in U.S. dollars), the EMBI (sovereign and quasi-sovereign debt
denominated in U.S. dollars), and the GBI (sovereign debt denominated in local currency).
Appendix Figure 1 presents the assets under management of funds that track their
performance against J.P. Morgan indexes divided by family type. Appendix Table 2
presents the different requirements that a bond must fulfill to enter the most popular J.P.
Morgan indexes in this segment: the CEMBI Broad Diversified, the CEMBI Narrow
Diversified, and the EMBI Global Diversified.
37
Appendix 2. Fund Classification with Morningstar Direct Mutual Fund Data
We classify Morningstar funds into emerging market corporate specialists and cross-over
categories. The cross-over category is also sub-divided into developed markets, emerging
market sovereign, and emerging market mixed. To categorize funds, we use the
Morningstar “global category,” which it created by analyzing the composition of mutual
fund portfolios. We consider a fund as emerging market if its global category in
Morningstar is “Emerging Markets Fixed Income,” “Africa Fixed Income,” “India Fixed
Income,” “Latin America Fixed Income,” or “Mexico Fixed Income.” We classify the
other funds in the database as developed markets.
Emerging market funds are subdivided into corporate, sovereign, and mixed funds,
using the Morningstar variable “primary prospectus benchmark.” This variable indicates
which index or group of indexes a fund is benchmarked against. If a fund is solely
benchmarked against a corporate (sovereign) bond index or indexes, it is classified as
corporate (sovereign). If a fund is benchmarked against a bond index that follows both
corporate and sovereign bonds (disregarding their share in the index) or a group of indexes
that include corporate and sovereign indexes, it is classified as mixed.
To determine whether the funds are benchmarked against a corporate, sovereign,
or mixed bond index or indexes, we used the following guidelines. J.P. Morgan CEMBI
indexes and indexes with “corporate” or “non-sovereign” in their name are classified as
corporate. J.P. Morgan EMBI and GBI-EM indexes are classified as sovereign. J.P. Morgan
ELMI+ indexes are classified as mixed because they are money market indexes. Indexes
with “government,” “treasury,” “sovereign,” or a similar term in their name are classified
as sovereign. For the 90 funds in the database that do not fall into the guidelines described
above or whose “primary prospectus benchmark” is not available, we searched manually
the composition of their holdings through Morningstar, the Financial Times, or the official
fund’s website to determine whether the fund should be classified as corporate, sovereign,
38
or mixed. If a fund only holds corporate (sovereign) bonds in its portfolio, it is classified
as corporate (sovereign). If a fund holds both corporate and sovereign bonds, it is classified
as mixed.
Panel A. Cumulative Distribution of Issuance Size
Panel B. Effect of an Increase in the Size Yield Discount (D)
Figure 1Model-Based Cumulative Distribution of Issuance Size
This figure plots the model-based cumulative distribution of issuance size. I denotes issuance size,G(I) denotes the cumulative distribution of issuance size, and X denotes firm size. Firms of size belowX issue X, firms in the size interval [X:500) issue 500, and firms of size greater or equal than 500 issueX. Because of the opportunity cost of cash, there are no bond issuances of size [X:500). Panel A plotsthe cumulative distribution of issuance size for a given X. Panel B shows how the cumulativedistribution of issuance size changes when the size yield discount (D) increases (which decreases X).
G(I)
G(I)
F(500)
F(𝑋)
500
I
I
𝐺 𝐼 𝑓𝑜𝑟 𝑋 𝐺 𝐼 𝑓𝑜𝑟 𝑋 < 𝑋
𝑋
𝑋
500
𝑋
Figure 2Yield to Maturity of U.S. Dollar Bonds Issued by Emerging Markets
This figure shows the yield to maturity of international U.S. dollar-denominated bonds issued by firms in emergingmarkets during 2000-16. The lines show the average yield to maturity of bonds issued with face values below $300(0:300), between $300 and below $500 [300:500), and equal to or above $500 [500:1,000) million, respectively.
3
4
5
6
7
8
9
Yie
ld to
Mat
urity
(%)
(0:300) [300:500) [500:1,000)
Panel B. Developed Markets
Figure 3Yield to Maturity of Issuances, Pre and Post 2008
This figure shows the average yield to maturity of international U.S. dollar-denominated bonds of differentissuance sizes in millions of U.S. dollars for firms in emerging markets (Panel A) and investment grade developedmarket firms (Panel B) during the pre-2008 (2000-08) and post-2008 (2009-16) periods.
Panel A. Emerging Markets
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
[100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
Yie
ld to
Mat
urity
(%)
Issuance Size
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
[100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
Yie
ld to
Mat
urity
(%)
Issuance Size
Pre-2008 Period Post-2008 Period95% CI 95% CI
Panel A. Total Value of Bonds
Panel B. Total Number of Bonds
Figure 4Value and Number of U.S. Dollar Bonds Issued by Emerging Markets
This figure shows the total value (Panel A) and the total number (Panel B) of international U.S. dollar-denominatedbonds issued by firms in emerging markets (EM) during 2000-16. The areas represent bonds issued with differentface values in millions of U.S. dollars. The total value of bonds is measured in billions of 2011 U.S. dollars.
0
20
40
60
80
100
120
140
Tot
al V
alue
of
Bon
ds
0
50
100
150
200
250
300
350
400
450
Tot
al N
umbe
r of
Bon
ds
(0:300) [300:500) [500:1,000)
Panel A. Emerging Markets
Panel B. Developed Markets
Figure 5Cumulative Distribution of Issuance Size, Pre and Post 2008
This figure shows the cumulative distribution of international U.S. dollar-denominated bond issuances by size inmillions of U.S. dollars for emerging market (Panel A) and investment grade developed market firms (Panel B)during the pre-2008 (2000-08) and post-2008 (2009-16) periods.
0.0
0.2
0.4
0.6
0.8
1.0
0 100 200 300 400 500 600 700 800 900
Cum
ulat
ive
Den
sity
Issuance Size
0.0
0.2
0.4
0.6
0.8
1.0
0 100 200 300 400 500 600 700 800 900
Cum
ulat
ive
Den
sity
Issuance Size
Pre-2008 Period Post-2008 Period
Panel B. Issuances
Panel A. Yield to Maturity
Figure 6Yield to Maturity and Bond Issuances of $500 Million
This figure shows the evolution of the yield to maturity and the percentage of international $500 million bondissuances during 2000-16. Panel A displays the average yield to maturity in each year for $500 million bondissuances. Panel B shows the average number of bond issuances of size equal to $500 million, relative to the totalnumber of bond issuances. The series display percent values for emerging market firms and investment gradedeveloped market firms during 2000-16.
0
4
8
12
16
20
Num
ber
of $
500
Mill
ion
Bon
ds/
Tot
al N
umbe
r of
Bon
d Is
suan
ces
(%)
Emerging Markets Developed Markets
2
4
6
8
10
Yie
ld to
Mat
urity
(%)
Panel A. Flows into Emerging Market Debt and Share of $500 Million Bonds
Panel B. Weight in Emerging Markets of Developed Market Mutual Funds
Figure 7Mututal Fund Investments in Emerging Markets
Panel A shows the cumulative flows into emerging market sovereign and corporate debt funds in billions of U.S.dollars and the share of international $500 million bond issuances during 2003-16. The latter share is calculated asthe number of international $500 million bonds issued by emerging market firms relative to all international dollar-denominated bonds issued by these firms. The correlation coefficients between the two time series are reported atthe top of the figure. Panel B shows the weight in emerging markets of developed market mutual funds during2005-16.
0
30
60
90
120
0
5
10
15
20
25
Cum
ulat
ive
Flo
ws
Shar
e of
$50
0 M
illio
n B
onds
(%)
Number of $500 Million Bonds/Total Number of Bonds (Left Axis)
Cumulative Flows to Emerging Market Debt Funds (Right Axis)
Correlation = 0.93
0
5
10
15
20
Wei
ght
(%)
Median Exposure to Emerging Markets of Developed Market Funds
Figure 8Size Distribution of Issuers of Different Bond Sizes
This figure shows the firm size distribution of emerging and investment grade developed market issuers of international U.S. dollar-denominated bonds of different sizes during thepre-2008 (2000-08) and post-2008 (2009-16) periods. Firm size is measured by the log of total assets. The left-side graphs show the cumulative distribution of issuers ofinternational bonds with a face value below $500 million (0:500). The right-side graphs show the cumulative distribution of issuers of international bonds with a face value equal toor above $500 million [500:1,000). Issuers in each sub-period are defined as firms that issued bonds of a certain size at least once during this period. Densities are estimated usingthe Epanechnikov kernel function.
Panel A. Emerging Markets
Panel B. Developed Markets
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
10 15 20 25 30
Log of Issuer Size
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
5 10 15 20 25 30 35
Den
sity
Log of Issuer Size
Issuers of (0:500) Bonds Issuers of [500:1,000) Bonds
0.00
0.05
0.10
0.15
0.20
0.25
5 10 15 20 25 30 35
Den
sity
Log of Issuer SizePre-2008 Period Post-2008 Period
0.00
0.05
0.10
0.15
0.20
0.25
10 15 20 25 30 35
Log of Issuer Size
Figure 9Change in Cash and Short-Term Investments After Issuance
This figure shows the coefficients of cash and short-term investments from firm-level panel OLS regressions that measure the use of funds forinternational U.S. dollar-denominated bond issuers two periods after the issuance. Panel A shows the use of funds for emerging market issuersand investment grade developed market issuers of bonds of different sizes during the pre-2008 (2000-08) and post-2008 (2009-16) periods. Thegraph shows the dollar effect separately for issuers of international bonds with a face value below $500 million (0:500) and equal to or above $500million [500:1,000). Panel B shows the use of funds for bond issuers in emerging markets during 2009-16, separately for firms with high and lowassets. A firm is classified as a high asset firm if their average asset value for the period 2009-16 is more than or equal to the median of the assetsof all firms that issued equal to or above $500 million bonds within 2009-16 in the same country. The analysis follows the specification of Kimand Weisbach (2008). The dependent variable for year t is Y = log[((Vt - V0)/Assets) + 1], where V is cash and short-term investments.Independent variables are bond issuance value and other sources of funds, both normalized by total assets, in addition to the log of total assets.Panel A also controls for the contemporaneous growth rate of sales, and the standard deviation of the growth of sales. Total assets are measuredat the value of the year just before the issuance. The dollar effect captures the dollar change in the dependent variable resulting from a one dollarincrease in a firm’s bond issuance. All variables are winsorized at the 1% level.
Panel A. Controlling for Growth of Sales and the Standard Deviation of the Growth of Sales
Panel B. High and Low Asset Emerging Market Firms
0.41
0.12
0.67
0.49
0.25
0.71
0.54
0.34
0.0
0.2
0.4
0.6
0.8
(0:500) [500:1,000) (0:500) [500:1,000)
Emerging Markets Developed Markets
$ C
hang
e
Pre-2008 Period Post-2008 Period
0.06
0.81
0.0
0.4
0.8
1.2
$ C
hang
e
High Asset Firms Low Asset Firms
Pre 2008 Post 2008 Pre 2008 Post 2008
(0:300) 42.86% 16.64% 75.41% 47.52%
[300:500) 23.99% 21.72% 13.41% 19.67%
[500:1,000) 33.15% 61.64% 11.18% 32.81%
Table 1Emerging Market Bonds of Different Size
This table reports the percentage of international U.S. dollar-denominated bonds issued with facevalues below $300 (0:300), between $300 and below $500 [300:500), and equal to or above $500[500:1,000) million, by firms in emerging markets (EM) during the pre-2008 (2000-08) and post-2008 (2009-16) periods. Column (1) displays the percentage of the total value of bonds issued ineach size category relative to the total value of bonds issued of any size. The value of each bond isin constant 2011 U.S. dollars. Column (2) displays the percentage of the total number of bondsissued in each size category relative to the total number of bonds issued of any size.
(1)Total Value of Bonds
(2)Total Number of Bonds
Emerging Markets 7.189 6.223 -0.966 *** 7.100 4.922 -2.177 *** -1.211 ***(0.232) (0.189) (0.312) (0.180) (0.078) (0.189) (0.333)
Developed Markets 5.534 4.076 -1.458 *** 5.357 3.676 -1.681 *** -0.223(0.077) (0.075) (0.109) (0.068) (0.058) (0.092) (0.147)
Emerging Markets 0.043 0.063 0.020 *** 0.065 0.188 0.123 *** 0.103 ***(0.004) (0.005) (0.006) (0.005) (0.008) (0.009) (0.011)
Developed Markets 0.043 0.047 0.004 0.074 0.110 0.035 *** 0.031 ***(0.002) (0.003) (0.003) (0.003) (0.004) (0.005) (0.006)
0.072 ***(0.013)
(5) (6)=(5)-(4)Pre 2008 Post 2008 Diff Pre 2008 Post 2008 Diff
-0.988 ***(0.344)
Panel B. Issuance
[400:500) [500:600) Diff-in-Diff Triple Diff
(7)=(6)-(3) (8)=EM(7)-DM(7)(1) (2) (3)=(2)-(1) (4)
(7)=(6)-(3) (8)=EM(7)-DM(7)(1) (2) (3)=(2)-(1) (4) (5) (6)=(5)-(4)Pre 2008 Post 2008 Diff Pre 2008 Post 2008 Diff
Table 2Yield to Maturity and Probability of Issuing [400:500) and [500:600) Bonds
This table reports mean tests for the yield to maturity and the probability of issuing an international U.S. dollar-denominated bond with a face value between $400 andbelow $500 [400:500), and between $500 and below $600 [500:600) million, for firms in emerging markets (EM) and investment grade firms in developed markets (DM)during the pre-2008 (2000-08) and post-2008 (2009-16) periods. Panel A shows the yield to maturity (in percent) in each category. Panel B shows the percentage of issuingbonds. Columns (1)-(3) show the mean tests and differences (pre and post-2008) for the [400:500) bonds separately for emerging and developed markets. Columns (4)-(6)show the mean tests and differences (pre and post-2008) for the [500:600) bonds. Column (7) shows the difference-in-differences effects between columns (3) and (6) foreach region. Column (8) reports the triple difference between emerging and developed markets. The yield to maturity variable is winsorized at the 5% level. *, **, and ***indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A. Yield to Maturity
[400:500) [500:600) Diff-in-Diff Triple Diff
EM*[100:200)*Post 2008 -0.778 -0.161 0.469 -1.394(0.619) (0.425) (0.340) (0.921)
EM*[200:300)*Post 2008 0.27 0.339 0.863 *** -0.335(0.261) (0.301) (0.305) (0.336)
EM*[300:400)*Post 2008 -0.0342 -0.293 0.435 * -0.942 **(0.235) (0.277) (0.253) (0.462)
EM*[400:500)*Post 2008 0.492 0.872 ** 1.154 *** 0.132(0.366) (0.366) (0.339) (0.503)
EM*[500:600)*Post 2008 -0.496 *** -0.052 0.397 -0.913 *(0.153) (0.409) (0.308) (0.474)
EM*[600:700)*Post 2008 0.915 * 0.588 0.516 -1.720 *(0.516) (0.513) (0.454) (0.993)
EM*[700:800)*Post 2008 0.820 ** 0.176 0.531 1.944 ***(0.380) (0.586) (0.451) (0.630)
EM*[800:900)*Post 2008 0.752 0.850 1.275 * 1.401(0.540) (0.983) (0.645) (0.916)
Bond Controls
Country FE
Industry-Year FE
Maturity FE
Ratings FE
Quarter-Year FE
-0.988 ** -0.924 ** -0.757 ** -1.045
0.161
4,037
R2 0.344 0.659 0.763 0.347
Number of Observations 7,939 7,939 7,818
Diff-in-Diff
P-Value 0.002 0.021 0.029
No No Yes No
No Yes Yes No
No Yes Yes No
No No Yes No
No No Yes No
No Yes Yes No
Table 3Yield to Maturity and Issuance Size Across Time
Associated Coefficients
Dependent Variable: Yield to Maturity
(1) (2) (3) (4)
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 , − 𝛽 ,
This table reports difference-in-difference regressions of the yield to maturity of international U.S. dollar-denominated bonds of different bucket sizes in millions of U.S. dollars, measuring the relative change after 2008for firms in emerging markets (EM) compared to investment grade firms in developed markets (DM). Theanalysis is restricted to positive issuance observations during 2000-16 and includes both emerging market andinvestment grade developed market issuers. The full equation estimated is Equation (7) in the text. Columns (1)-(4) report the coefficients 𝛽 , of the interaction term between the dummy of each bucket size, the post2008 dummy (equal to 1 for 2009-16) and the emerging market dummy. The coefficients 𝛽 ,
𝛽 , (Appendix Table 5) and 𝛽 (Appendix Table 6) are estimated but are not reported in this table toconserve space. Column (2) includes country, industry-year, and quarter-year fixed effects (FE). Column (3)includes country, industry-year, maturity, rating, quarter-year fixed effects, in addition to bond-firm controls.Bond-firm controls include a dummy indicating whether the bond was issued privately or publicly, a dummyindicating whether the firm is foreign-owned, a dummy indicating whether the firm has partial governmentownership, and a fixed or flexible coupon dummy. Column (4) reports a placebo test using non-index-eligiblebonds, which are those with less than five years of maturity or flexible coupon rates. Standard errors areclustered at the country and quarter-year levels. The yield to maturity variable is winsorized at the 5% level. *, **,and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
EM*Post 2008 -0.090 -0.075 ** -0.012 0.038 ** 0.015 0.090 *** -0.001 0.019 0.012(0.056) (0.029) (0.020) (0.015) (0.016) (0.023) (0.009) (0.012) (0.009)
Bond Controls
Country FE
Industry FE
Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2
EM*Post 2008 -0.074 -0.076 ** -0.009 0.037 ** 0.014 0.084 *** -0.003 0.013 0.011(0.058) (0.029) (0.019) (0.015) (0.016) (0.024) (0.009) (0.013) (0.009)
Bond Controls
Country FE
Industry FE
Maturity FE
Quarter-Year FE
Rating FE
Mean Probability
Number of Countries
Number of Observations
R2
Table 4Probability of Issuing Bonds in Each Bucket
This table reports difference-in-difference regressions of the change in the probability of issuing an international U.S. dollar-denominatedbond of a certain size interval in millions of U.S. dollars, pre and post 2008, for firms in emerging markets (EM) relative to investmentgrade firms in developed markets (DM). The analysis is restricted to positive issuance observations during 2000-16. Panel A reports theregressions for the probability of issuing a bond of a certain size as a dependent variable on the interaction of the post 2008 dummy (equalto 1 for 2009-16) with the emerging market dummy. Regressions include country, industry and quarter-year fixed effects (FE). Panel Breports the same regression as in Panel A but including maturity and rating fixed effects, in addition to bond-firm controls. Bond-firmcontrols include a dummy indicating whether the bond was issued privately or publicly, a dummy indicating whether the firm is foreign-owned, a dummy indicating whether the firm has partial government ownership, and a fixed or flexible coupon dummy. Standard errorsare clustered at the country and quarter-year levels. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels,respectively.
Panel A. Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
No No No No No No No No No
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
Yes Yes
0.359 0.173 0.127 0.0908 0.0471 0.101 0.0296 0.0491 0.0157
68 68 68 68 68 68 68 68 68
19,905 19,905 19,905
0.218 0.0701 0.0457 0.0539 0.0407 0.0624 0.0371
19,905 19,905 19,905 19,905 19,905 19,905
0.0463 0.0271
Panel B. Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
Yes Yes
Yes Yes Yes Yes Yes Yes
0.359 0.173 0.127 0.0908 0.0471 0.101 0.0296 0.0491 0.0157
68 68 68 68 68 68 68 68 68
0.0660 0.0327
19,724 19,724 19,724
0.264 0.0734 0.0471 0.0553 0.0429 0.0853 0.0448
19,724 19,724 19,724 19,724 19,724 19,724
Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
EM*Post 2008 0.086 -0.053 * 0.009 0.016 -0.003 -0.014 -0.015 ** -0.028 ** -0.001(0.054) (0.029) (0.027) (0.019) (0.014) (0.021) (0.007) (0.011) (0.010)
Country FE
Industry FE
Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2
13,993
0.202 0.0683 0.0646 0.0767 0.0533 0.0450 0.0393 0.0371 0.0303
13,993 13,99313,993 13,993 13,993 13,993
66 66 66 66 66
13,993
0.0677 0.0170 0.0247
13,993
0.00850
66 66 6666
0.425 0.204 0.130 0.0799 0.0391
Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes YesYes Yes Yes Yes
Yes Yes Yes Yes Yes
Yes
[500:600) [600:700) [700:800)
Yes
[800:900)
Yes Yes YesYes
(0:100) [100:200) [200:300) [300:400) [400:500)
Table 5Probability of Issuing Non-Index-Eligible Bonds in Each Bucket
This table reports difference-in-difference regressions of the change in the probability of issuing an international U.S. dollar-denominated bond of a certain size interval in millions of U.S. dollars, pre and post-2008, for firms in emerging markets (EM) relativeto invesetment grade firms in developed markets (DM). The analysis is restricted to positive issuance observations during 2000-16.The table reports regressions for the bond issuance dummy of each size bucket on the interaction of the post 2008 dummy (equal to 1for 2009-16) with the emerging market dummy. As opposed to the results in Table 4, the regressions in this table use only non-index-eligible bonds, which are those with less than five years of maturity or flexible interest rates. All regressions include country, industry,and quarter-year fixed effects (FE). Standard errors are clustered at the country and quarter-year levels. *, **, and *** indicatestatistical significance at the 10%, 5%, and 1% levels, respectively.
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Dependent Variable: Dummy=1 if Issuance=[X:X+100)
(4)
Log(1 + Lagged Carry Trade) 0.023 *** 0.052 *** 0.055 *** 0.093 ***(0.005) (0.012) (0.012) (0.014)
Maturity controls No No Yes Yes
Industry FE No Yes Yes Yes
Rating FE No No No Yes
Quarter-Year FE Yes Yes Yes Yes
Number of Observations 1,331 1,322 1,286 1,284
R2 0.043 0.103 0.108 0.172
Table 6Probability of Issuing $500 Million Bonds and the Carry Trade
This table reports linear regressions of the probability of issuing an international U.S. dollar-denominated bond on the log of one plus the lagged carry trade measure for firms in emergingmarkets (EM) during 2009-16. The estimations include only issuances of bonds of less than orequal to $500 million in face value. The carry trade measure is the difference between the interestrate in the money market in local currency and the U.S. money market rate, divided by the annualvolatility of the exchange rate during the previous quarter. Regressions in column (1) includequarter-year fixed effects (FE). Regressions in column (2) include industry and quarter-year fixedeffects. Regressions in columns (3) and (4) include maturity controls and industry, rating, andquarter-year fixed effects. Maturity controls use the maturity in years of each bond, measured asthe number of years to final maturity. Standard errors are clustered at the country and time levels.Standard errors are clustered at the country and quarter-year level. *, **, and *** indicate statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
Dependent Variable: Dummy=1 if Issuance=500
(1) (2) (3)
All FundsWith Emerging
Market Corporate Debt
All FundsWith Emerging
Market Corporate Debt
AllEmerging
Market
(1) (2) (3) (4) (5) (6) (7)=(3)*(6)
Cross-Over Funds
Developed Markets 895 580 4,201,066 3,456,386 33.56% 1.75% 73,712
Emerging Market Sovereign 315 265 278,354 273,884 21.78% 16.22% 45,156
Emerging Market Mixed 129 101 49,045 41,259 26.74% 19.49% 9,560
Emerging Market Specialists Funds
Emerging Market Corporate Specialists 96 91 32,593 32,364 87.66% 63.86% 20,815
All 1,435 1,037 4,561,058 3,803,894 33.15% 3.27% 149,249
Table 7Number, Size, and Portfolio Composition of Debt Fund Categories
Number of Funds Fund Size Corporate Debt
Emerging Market
Corporate Debt
This table reports the number of funds, their size, and the portfolio composition of each category of mutual funds at the end of December 2016. The sample is restricted to fixedincome funds. Columns (1), (3), (5)-(7) consider the full sample of fixed income funds. Columns (2) and (4) restrict the sample to funds with at least one emerging market corporatedebt security in their portfolio. Column (5) reports the percentage of corporate debt over total debt for each category of funds. Columns (6) and (7) report the percentage and thetotal value of the emerging markets corporate debt, respectively. The size of the funds and the value of corporate debt is computed from the market value in millions of U.S.dollars. Corporate debt is composed of corporate, corporate inflation projected, and undefined bond securities. Corporate debt is considered emerging market (EM) corporate debtwhen it is issued by an emerging market firm.
Compared to Outstanding
Amount
(5)
Cross-Over Funds
Developed Markets 6.70 % 10.73 % 82.57 % -13.540 *** 14.420(19.157) (22.718) (28.686) (2.469)
Emerging Market Sovereign 6.31 % 15.45 % 78.24 % -9.209 *** 10.089(12.163) (19.719) (22.901) (2.660)
Emerging Market Mixed 7.40 % 18.29 % 74.31 % -5.284 6.164(15.371) (18.557) (23.230) (3.261)
Emerging Market Specialists Funds
Emerging Market Corporate Specialists 6.32 24.65 69.03 0.880(9.071) (15.520) (18.690)
Total Amount Outstanding 12.407% 19.444% 68.149% -
Table 8Percentage of Emerging Market Corporate Debt in a Certain Bucket
International U.S. Dollar-Denominated Bonds
(4)
-
Differences
Compared to EM Specialists
This table reports the percentage of emerging market corporate debt of a certain size interval over the total value of emerging market corporate debtissued of any size that the mean fund of each category holds in its portfolio at the end of December 2016. The analysis is restricted to fixed incomefunds and to international U.S. dollar-denominated bonds. Columns (1), (2), and (3) report the percentage of emerging market corporate bondsissued with face values below $300 (0:300), between $300 and below $500 [300:500), and equal to or above $500 [500:1,000) million, respectively.Corporate emerging market debt is composed by corporate, corporate inflation projected, and undefined bond securities issued by an emergingmarket firm. Column (4) reports the differences in means tests for bond issuances with a face value equal to or over $500 million for each of fundswith respect to the emerging market corporate funds. Column (5) reports the difference in the share of the portfolio in each bucket size for eachcategory of funds with respect to the total amount outstanding. The total amount outstanding is the U.S. dollar-denominated outstanding value ofall the international corporate emerging market bonds included in Thomson Reuters Security Data Corporation Platinum database (SDC Platinum)at the end of 2016.
(0:300)
(1)
Total Value of Emerging Market Corporate Debt
[500:1,000)
(3)
[300:500)
(2)
Pre 2008 -5.683 *** -4.241 *** -10.356 *** -7.257 ***(0.512) (0.262) (0.979) (0.474)
Post 2008 -4.395 *** -3.701 *** -7.426 *** -6.218 ***(0.318) (0.250) (0.578) (0.437)
Pre 2008 * ln(Assets) 0.501 *** 0.368 *** 0.921 *** 0.631 ***(0.055) (0.027) (0.102) (0.048)
Post 2008 * ln(Assets) 0.420 *** 0.323 *** 0.711 *** 0.544 ***(0.035) (0.026) (0.063) (0.044)
Number of Observations 1,688 2,240 1,688 2,240
This table reports the probit and logit regressions of the change in the probability of issuing an international U.S. dollar-denominated bond with aface value equal to or above $500 [500:1,000) million in the pre-2008 (2000-08) and post-2008 (2009-16) periods, for firms in emerging markets(EM) and investment grade firms in developed markets (DM). The data are aggregated by firm and sub-period (pre-2008 and post-2008) level. Thesample is restricted to firms that issued at least one bond during 2000-16. Columns (1)-(4) report regressions for bond issuances of equal to or over$500 millions dummy as a dependent variable on the pre-2008 dummy, the post-2008 dummy, and the interaction of the pre and post dummyvariables with the log of assets. Assets are computed as the mean value per firm and sub-period. Columns (1) and (2) report probit regressions.Columns (3) and (4) report logit regressions. Robust standard errors are reported. *, **, and *** indicate statistical significance at the 10%, 5%, and1% levels, respectively.
Table 9Probability of Issuing Large U.S. Dollar-Denominated Bonds
(3) (4)
Developed MarketsEmerging Markets
Probit Regression Coefficients Logit Regression Coefficient
Emerging Markets Developed Markets
(1) (2)
Dependent Variable: Dummy=1 if Issuance=[500:1,000)
Appendix Figure 1Assets Benchmarked to J.P. Morgan Emerging Market Debt Indexes
This figure shows the evolution of the assets of funds that track their performance against J.P. Morgan's emerging marketdebt indexes during 2007-18. Numbers are in billions of U.S. dollars. CEMBI stands for Corporate Emerging Market BondIndex, EMBI stands for Emerging Market Bond Index, and GBI stands for Goverment Bond Index.
0
50
100
150
200
250
300
350
400
0
10
20
30
40
50
60
70
80
CEMBI Broad (Left Axis) CEMBI Narrow (Left Axis)
GBI (Right Axis) EMBI (Right Axis)
Panel A. Yield to Maturity of Issuances, Pre and Post 2008
Appendix Figure 2Including High-Yield Developed Market Bonds into the Emerging Market Sample
This figure shows the average yield to maturity (Panel A) and the cumulative distribution (Panel B) of internationalU.S. dollar-denominated bonds of different issuance sizes in millions of U.S. dollars, for emerging market issuersand high-yield developed market issuers during the pre-2008 (2000-08) and post-2008 (2009-16) periods.
Panel B. Cumulative Distribution of Issuance Size, Pre and Post 2008
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
[100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
Yie
ld to
Mat
urity
(%)
Issuance Size
Pre-2008 Period Post-2008 Period
95% CI 95% CI
0.0
0.2
0.4
0.6
0.8
1.0
0 100 200 300 400 500 600 700 800 900
Cum
ulat
ive
Den
sity
Issuance Size
Pre-2008 Period Post-2008 Period
Developed Markets
Argentina Mongolia AustraliaAzerbaijan Morocco AustriaBahrain Nigeria BelgiumBrazil Oman CanadaChile Panama DenmarkChina Peru FinlandColombia Philippines, The FranceCroatia Poland GermanyCzech Republic Qatar GreeceDominican Republic Russian Federation Hong Kong SAR, ChinaEgypt, Arab Rep. Saudi Arabia IcelandEl Salvador Singapore IrelandGuatemala South Africa ItalyHungary Taiwan, China JapanIndia Thailand LuxembourgIndonesia Trinidad and Tobago NetherlandsIsrael Turkey New ZealandJamaica Ukraine NorwayKazakhstan United Arab Emirates PortugalKorea, Rep. Venezuela, RB SpainKuwait SwedenLebanon SwitzerlandMalaysia United KingdomMexico United States
Appendix Table 1List of Countries
This table displays the list of markets classified as emerging and developed in the sample.
Emerging Markets
CEMBI Broad/ Broad Div. CEMBI / Div (Narrow)
Country/RegionGNI per capita must be below the Index IncomeCeiling (IIC) for three consecutive years.
Issuer N/A
Liquidity N/A N/ADaily available pricing from third party evaluationvendor.
Instrument TypeAll fixed, floaters, amortizers, andcapitalizers.Defaulted bonds are excluded.
All fixed, bullets (only two largestinstruments from any issuer).Defaulted bonds are excluded.
All fixed, floaters, amortizers, capitalizers, andloans.
Minimum Outstanding Face Value Amount
$300 Million $500 Million $500 Million
Maturity ● Enter when at least two and a half years to maturity.● Exit when less than one year to maturity.
Law/Settlement N/A
Includes Quasi-Sovereign Bonds
YesN/A
Local law instruments are not eligible; Euroclearable or settledthrough another institution outside issuing country.
Appendix Table 2CEMBI and EMBI Requirements
This table reports the requirements for bonds to qualify for inclusion in the J.P. Morgan CEMBI and EMBI indexes.
CEMBI
Issuer needs to belong to a country in one of the following regions:Asia ex Japan, Latam, Eastern Europe, Middle East/Africa.
● Headquartered in an emerging market (EM) country, or● 100% of the issuer's asset are within EM economies, or● 100% secured by assets within EM economies.
● Enter when at least five years to maturity.● Exit when less than thirteen months to maturity.
EMBIG Diversified
Emerging Markets 7.189 6.223 -0.966 *** 7.089 4.883 -2.206 *** -1.240 ***(0.232) (0.189) (0.312) (0.188) (0.079) (0.195) (0.338)
Developed Markets 5.534 4.076 -1.458 *** 5.270 3.602 -1.669 *** -0.211(0.077) (0.075) (0.109) (0.073) (0.061) (0.098) (0.150)
Emerging Markets 0.043 0.063 0.020 *** 0.060 0.179 0.120 *** 0.099 ***(0.004) (0.005) (0.006) (0.005) (0.007) (0.009) (0.011)
Developed Markets 0.043 0.047 0.004 0.067 0.100 0.033 *** 0.028 ***(0.002) (0.003) (0.003) (0.003) (0.004) (0.005) (0.006)
0.071 ***(0.013)
(5) (6)=(5)-(4)
Pre 2008 Post 2008 Diff Pre 2008 Post 2008 Diff
-1.029 ***(0.344)
Panel B. Issuance
[400:500) [500:550) Diff-in-Diff Triple Diff
(7)=(6)-(3) (8)=EM(7)-DM(7)(1) (2) (3)=(2)-(1) (4)
(7)=(6)-(3) (8)=EM(7)-DM(7)(1) (2) (3)=(2)-(1) (4) (5) (6)=(5)-(4)
Pre 2008 Post 2008 Diff Pre 2008 Post 2008 Diff
Appendix Table 3Yield to Maturity and Probability of Issuing [400:500) and [500:550) Bonds
This table reports mean tests for the yield to maturity and the probability of issuing an international U.S. dollar-denominated bond issued with face values between$400 and below $500 [400:500), and between $500 and below $550 [500:550) million, for firms in emerging markets (EM) and investment grade firms in developedmarkets (DM) during the pre-2008 (2000-08) and post-2008 (2009-16) periods Panel A shows the yield to maturity (in percent) in each category. Panel B shows thepercentage of issuing bonds. Columns (1)-(3) show the mean tests and differences (pre and post-2008) for the [400:500) bonds separately for emerging and developedmarkets. Columns (4)-(6) show the mean tests and differences (pre and post-2008) for the [500:550) bonds. Column (7) shows the difference-in-differences effectsbetween columns (3) and (6) for each region. Column (8) reports the triple difference between emerging and developed markets. The yield to maturity variable iswinsorized at the 5% level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A. Yield to Maturity
[400:500) [500:550) Diff-in-Diff Triple Diff
EM*Post 2008 -0.001 -0.071 * -0.041 ** 0.012 -0.031 * 0.092 *** 0.010 0.029 -0.005(0.036) (0.038) (0.019) (0.025) (0.017) (0.026) (0.016) (0.018) (0.007)
Bond Controls
Frim FE
Maturity FE
Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2
[700:800) [800:900)
Appendix Table 4Probability of Issuing Bonds in Each Bucket
Adding Firm Fixed EffectsThis table reports difference-in-difference regressions of the change in the probability of issuing an international U.S. dollar-denominated bond of a certain size interval in millions of U.S. dollars, pre and post 2008, for firms in emerging markets (EM) relativeto investment grade firms in developed markets (DM). The analysis is restricted to positive issuance observations during 2000-16. Thetable reports regressions for the bond issuance dummy of each size bucket on the interaction of the post 2008 dummy (equal to 1 for2009-16) with the emerging market dummy. All regressions include bond-firm controls, namely, a dummy indicating whether thebond was issued privately or publicly, a dummy indicating whether the firm is foreign-owned, a dummy indicating whether the firmhas partial government ownership, and a fixed or flexible coupon dummy. They also include firm, maturity, and quarterly-year fixedeffects (FE). Standard errors are clustered at the country and quarter-year levels. *, **, and *** indicate statistical significance at the10%, 5%, and 1% levels, respectively.
Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Yes Yes Yes Yes
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600)
Yes Yes Yes Yes Yes
[600:700)
Yes Yes Yes Yes Yes Yes
Yes Yes YesYes
Yes
Yes Yes Yes
Yes
Yes Yes Yes Yes Yes
Yes
0.367 0.171 0.125 0.0875 0.0466 0.101 0.0293 0.0503 0.0155
Yes YesYes Yes Yes Yes
68 68 68 68 68 68 68 68 68
0.335 0.407
17,022 17,022 17,022
0.364 0.363
17,022
0.563 0.345 0.342 0.365 0.397
17,022 17,022 17,022 17,022 17,022
[100:200) -1.155 *** -0.713 *** -0.294 ** -1.127 ***(0.279) (0.193) (0.135) (0.354)
[200:300) -0.825 *** -0.549 *** -0.352 *** -0.821 **(0.200) (0.166) (0.123) (0.318)
[300:400) -0.519 ** -0.377 ** -0.244 -0.434(0.201) (0.180) (0.147) (0.327)
[400:500) -0.468 ** -0.343 * -0.292 -0.528(0.221) (0.191) (0.182) (0.338)
[500:600) -0.645 *** -0.344 ** -0.195 * -0.697 **(0.158) (0.158) (0.111) (0.313)
[600:700) -0.271 -0.199 -0.219 -0.688 **(0.170) (0.180) (0.201) (0.331)
[700:800) -0.155 0.00758 -0.0814 -0.0716(0.184) (0.178) (0.144) (0.356)
[800:900) 0.000 0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000)
[100:200)*Post 2008 -0.977 ** -4.703 *** -5.968 *** -1.068 **(0.412) (0.259) (0.353) (0.438)
[200:300)*Post 2008 -0.845 *** -4.679 *** -6.028 *** -0.898 ***(0.205) (0.276) (0.341) (0.234)
[300:400)*Post 2008 -1.319 *** -4.847 *** -6.178 *** -1.432 ***(0.127) (0.276) (0.336) (0.171)
[400:500)*Post 2008 -1.458 *** -5.147 *** -6.224 *** -1.486 ***(0.154) (0.224) (0.288) (0.185)
[500:600)*Post 2008 -1.681 *** -5.181 *** -6.396 *** -2.046 ***(0.154) (0.235) (0.305) (0.225)
[600:700)*Post 2008 -1.907 *** -5.287 *** -6.308 *** -1.336 ***(0.251) (0.240) (0.341) (0.442)
[700:800)*Post 2008 -2.344 *** -5.701 *** -6.623 *** -3.019 ***(0.148) (0.292) (0.312) (0.298)
[800:900)*Post 2008 -2.048 *** -5.3 *** -6.505 *** -3.081 ***(0.214) (0.271) (0.331) (0.412)
Bond Controls
Country FE
Industry-Year FE
Maturity FE
Ratings FE
Quarter-Year FE
4,037
R2 0.344 0.659 0.763 0.347
Number of Observations 7,939 7,939 7,818
No No Yes No
No Yes Yes No
No Yes Yes No
No No Yes No
No No Yes No
No Yes Yes No
Appendix Table 5Yield to Maturity and Issuance Size Across Time
Associated Coefficients
Dependent Variable: Yield to Maturity
(1) (2) (3) (4)
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽
This table reports difference-in-difference regressions of the yield to maturity of international U.S. dollar-denominated bonds of different bucket sizes in millions of U.S. dollars, measuring the relative change beforeamd after 2008 for investment grade firms in developed markets (DM) compared to firms in emerging markets(EM). The analysis is restricted to positive issuance observations during 2000-16 and includes both emergingmarket and investment grade developed market issuers. The full equation estimated is Equation (7) in the text.Columns (1)-(4) report the coefficients 𝛽 of the dummy of each bucket size and the coefficients 𝛽 , ofthe interaction term between the dummy of each bucket size and the post 2008 dummy (equal to 1 for 2009-16).The coefficients 𝛽 (Appendix Table 6) and 𝛽 , (Table 3) are estimated but are not reported in this tableto conserve space. Column (2) includes country, industry-year, and quarter-year fixed effects (FE). Column (3)includes country, industry-year, maturity, rating, quarter-year fixed effects, in addition to bond-firm controls.Bond-firm controls include a dummy indicating whether the bond was issued privately or publicly, a dummyindicating whether the firm is foreign-owned, a dummy indicating whether the firm has partial governmentownership, and a fixed or flexible coupon dummy. Column (4) reports a placebo test using only non-index-eligible bonds, which are those with less than five years of maturity or flexible coupon rates. Standard errors areclustered at the country and quarter-year levels. The yield to maturity variable is winsorized at the 5% level. *, **,and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
𝛽 ,
EM*[100:200) 2.998 *** 2.942 *** 2.165 *** 3.033 ***(0.393) (0.499) (0.454) (0.408)
EM*[200:300) 2.592 *** 2.75 *** 2.014 *** 2.863 ***(0.369) (0.431) (0.425) (0.374)
EM*[300:400) 1.687 *** 2.479 *** 1.678 *** 1.675 ***(0.450) (0.489) (0.419) (0.537)
EM*[400:500) 1.655 *** 1.872 *** 1.278 *** 1.566 ***(0.448) (0.430) (0.387) (0.479)
EM*[500:600) 1.742 *** 1.946 *** 1.321 *** 2.181 ***(0.299) (0.545) (0.446) (0.244)
EM*[600:700) 0.478 1.18 ** 0.932 * 1.007(0.490) (0.586) (0.509) (0.665)
EM*[700:800) 0.544 1.776 ** 1.112 * 0.732(0.392) (0.757) (0.582) (0.719)
EM*[800:900) 0.406 1.086 0.489 0.581(0.557) (0.970) (0.604) (0.481)
Bond Controls
Country FE
Industry-Year FE
Maturity FE
Ratings FE
Quarter-Year FE
0.087 0.074 0.043 0.615
0.271
4,037
R2 0.344 0.659 0.763 0.347
Number of Observations 7,939 7,939 7,818
Diff-in-Diff
P-Value 0.722 0.838 0.875
No No Yes No
No Yes Yes No
No Yes Yes No
No No Yes No
No No Yes No
No Yes Yes No
Appendix Table 6Yield to Maturity and Issuance Size Across Time
Associated Coefficients
Dependent Variable: Yield to Maturity
(1) (2) (3) (4)
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽
𝛽 − 𝛽
This table reports difference-in-difference regressions of the yield to maturity of international U.S. dollar-denominated bonds of different bucket sizes in millions of U.S. dollars, measuring the relative change before2008 for firms in emerging markets (EM) compared to investment grade firms in developed markets (DM). Theanalysis is restricted to positive issuance observations during 2000-16 and includes both emerging market andinvestment grade developed market issuers. The full equation estimated is Equation (7) in the text. Columns(1)-(4) report the coefficients 𝛽 of the interaction term between the dummy of each bucket size and theemerging market dummy. The coefficients 𝛽 , 𝛽 , (Appendix Table 5) and 𝛽 , (Table 3) areestimated but are not reported in this table to conserve space. Column (2) includes country, industry-year, andquarter-year fixed effects (FE). Column (3) includes country, industry-year, maturity, rating, quarter-year fixedeffects, in addition to bond-firm controls. Bond-firm controls include a dummy indicating whether the bondwas issued privately or publicly, a dummy indicating whether the firm is foreign-owned, a dummy indicatingwhether the firm has partial government ownership, and a fixed or flexible coupon dummy. Column (4) reportsa placebo test using only non-index-eligible bonds, which are those with less than five years of maturity orflexible coupon rates. Standard errors are clustered at the country and quarter-year levels. The yield to maturityvariable is winsorized at the 5% level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%levels, respectively.
EM*Post 2008 0.012 -0.090 * -0.035 0.019 -0.020 0.083 *** 0.018 0.012 -0.001(0.042) (0.048) (0.024) (0.025) (0.015) (0.023) (0.013) (0.017) (0.009)
Bond Controls
Firm FE
Industry-Year FE
Maturity-Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2
EM*Post 2008 0.014 -0.087 * -0.027 0.017 -0.001 0.029 0.039 ** 0.003 0.005(0.047) (0.046) (0.032) (0.028) (0.019) (0.027) (0.016) (0.015) (0.009)
Bond Controls
Firm FE
Industry-Year FE
Ratings-Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2 0.481 0.440 0.5040.598 0.406 0.437 0.490 0.499 0.460
16,996 16,996 16,996 16,996 16,996 16,996 16,996 16,996 16,996
67 67 67 67 67 67 67 67 67
Yes Yes Yes
0.369 0.170 0.124 0.0869 0.0469 0.101 0.0295
Yes Yes Yes Yes Yes Yes
0.0507 0.0158
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
0.437 0.386 0.459
Panel B. Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5)
0.578 0.380 0.394 0.435 0.452 0.417
(6) (7) (8) (9)
17,147 17,147 17,147 17,147 17,147 17,147 17,147 17,147 17,147
68 68 68 68 68 68 68 68 68
Yes Yes Yes
0.367 0.170 0.125 0.0876 0.0469 0.101 0.0296
Yes Yes Yes Yes Yes Yes
0.0506 0.0157
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
[800:900)
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Appendix Table 7Probability of Issuing Bonds in Each Bucket
Controlling for Maturity-Time and Ratings-Time Fixed EffectsThis table reports difference-in-difference regressions of the change in the probability of issuing an international U.S. dollar-denominatedbond of a certain size interval in millions of U.S. dollars, pre and post 2008, for firms in emerging markets (EM) relative to investmentgrade firms in developed markets (DM). The analysis is restricted to positive issuance observations during 2000-16. The table reportsregressions for the bond issuance dummy of each size bucket on the interaction of the post 2008 dummy (equal to 1 for 2009-16) with theemerging market (EM) dummy. Regressions in Panel A include firm, industry-year, and maturity-quarter-year fixed effects (FE).Regressions in Panel B include firm, industry-year, and ratings-quarter-year FE. All the regressions include bond-firm controls, namely, adummy indicating whether the bond was issued privately or publicly, a dummy indicating whether the firm is foreign-owned, a dummyindicating whether the firm has partial government ownership, and a fixed or flexible coupon dummy. Standard errors are clustered at thecountry and quarter-year levels. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A. Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5) (6) (7) (8) (9)
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800)
EM*Post 2008 0.020 -0.071 -0.034 0.012 -0.021 0.073 *** 0.017 0.005 -0.003(0.039) (0.053) (0.022) (0.025) (0.015) (0.022) (0.013) (0.017) (0.010)
EM*U.S. Treasury Basis -0.195 *** -0.061 0.086 ** 0.099 ** 0.002 0.062 -0.011 0.025 0.000(0.061) (0.055) (0.042) (0.039) (0.033) (0.042) (0.022) (0.016) (0.016)
Bond Controls
Firm FE
Industry-Year FE
Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2
EM*Post 2008 -0.026 -0.099 -0.015 0.009 -0.005 0.096 *** 0.006 0.034 0.004(0.049) (0.061) (0.028) (0.033) (0.023) (0.029) (0.020) (0.022) (0.011)
EM*U.S. Treasury Basis -0.128 * -0.020 0.058 0.103 ** -0.021 0.029 0.005 -0.017 ** -0.010(0.068) (0.069) (0.058) (0.0411) (0.030) (0.037) (0.025) (0.008) (0.017)
-0.284 * -0.175 0.116 -0.020 0.100 0.142 -0.068 0.178 *** 0.041(0.158) (0.120) (0.113) (0.097) (0.075) (0.104) (0.071) (0.050) (0.031)
Bond Controls
Firm FE
Industry-Year FE
Quarter-Year FE
Mean Probability
Number of Countries
Number of Observations
R2 0.572 0.369 0.390 0.432 0.450 0.410 0.435 0.380 0.457
17,147 17,147 17,147 17,147 17,147 17,147 17,147 17,147 17,147
0.0296 0.0506 0.0157
68 68 68 68 68 68 68
0.367 0.170 0.125 0.0876 0.0469 0.101
68 68
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
EM*Post 2008* U.S. Treasury Basis
Yes Yes Yes Yes Yes Yes Yes Yes
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
0.434 0.380 0.457
Panel B. Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5)
0.572 0.369 0.390 0.432 0.449 0.410
(6) (7) (8) (9)
17,147 17,147 17,147 17,147 17,147 17,147 17,147 17,147 17,147
68 68 68 68 68 68 68 68 68
Yes Yes Yes
0.367 0.170 0.125 0.0876 0.0469 0.101 0.0296
Yes Yes Yes Yes Yes Yes
0.0506 0.0157
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
(0:100) [100:200) [200:300) [300:400) [400:500) [500:600) [600:700) [700:800) [800:900)
Appendix Table 8Probability of Issuing Bonds in Each Bucket
Demand for Safe U.S. Dollar Assets
This table reports difference-in-difference regressions of the change in the probability of issuing an international U.S. dollar-denominated bond of a certain size interval in millions of U.S. dollars, pre and post 2008, for firms in emerging markets (EM) relative toinvestment grade firms in developed makets (DM). The analysis is restricted to positive issuance observations during 2000-16. Panel Areports regressions for the bond issuance dummy of each size bucket on the interaction of the post 2008 dummy and the emergingmarket dummy and on the interaction of the U.S. Treasury basis and the emerging market dummy. Panel B reports the same type ofregressions but including the additional interaction of the post 2008 dummy, the emerging market dummy, and the U.S. treasury basis.The U.S. treasury basis is the the difference between the yield on a cash position in U.S. Treasurys and the synthetic dollar yieldconstructed from a cash position in a foreign bond, that is hedged back into dollars. It is computed as in Jian, Krishnamurthy and Lustig(2018). All regressions include bond-firm controls, namely, a dummy indicating whether the bond was issued privately or publicly, adummy indicating whether the firm is foreign-owned, a dummy indicating whether the firm has partial government ownership, and afixed or flexible coupon dummy. They also include firm, industry-year, and quarter-year fixed effects (FE). Standard errors are clusteredat the country and quarter-year levels. The yield to maturity variable is winsorized at the 5% level. *, **, and *** indicate statisticalsignificance at the 10%, 5%, and 1% levels, respectively.
Panel A. Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Probability of Issuing Debt of a Certain Amount
(1) (2) (3) (4) (5) (6) (7) (8) (9)
EM*Post 2008 -0.064 -0.070 ** -0.014 0.030 ** 0.012 0.079 *** -0.002 0.016 0.010(0.045) (0.027) (0.017) (0.014) (0.014) (0.020) (0.008) (0.010) (0.008)
Country FE
Industry FE
Quarter-Year FE
Number of Countries
Number of Observations
R2
22,095 22,095 22,095 22,095 22,095
0.170 0.0736 0.0459 0.0517 0.0365 0.0485 0.0312 0.0353 0.0233
Yes
22,095 22,095 22,095
Yes
68 68 68 68
22,095
68 68 68 68 68
Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes
[0:100) [100:200) [200:300) [300:400) [400:500)
Yes Yes Yes Yes Yes
[500:600) [600:700) [700:800) [800:900)
Appendix Table 9Unconditional Probabilities of Issuing a Bond in Each Bucket
This table reports difference-in-difference regressions of the change in the probability of issuing an international U.S. dollar-denominated bond of a certain size interval in millions of U.S. dollars, pre and post 2008, for firms in emerging markets (EM) relativeto the same change for investment grade firms in developed markets (DM), during the 2000-16 period. The analysis is restricted topositive and zero issuance observations. Columns (1)-(9) report regressions for the bond issuance dummy of each size bucket on thethe interaction of the post 2008 dummy (equal to 1 for 2009-16) with the emerging market dummy. All regressions include country,industry, and quarter-year fixed effects. Standard errors are clustered at the country and quarter-year levels. *, **, and *** indicatestatistical significance at the 10%, 5%, and 1% levels, respectively.
Unconditional Probability of Issuing Debt of a Certain Amount(1) (2) (3) (4) (5) (6) (7) (8) (9)
Dependent Variable: Dummy=1 if Issuance=[X:X+100)
Emerging Markets Developed Markets
Any Bucket Size 0.145 0.293
(0:300) 0.128 0.288
[300:500) 0.086 0.135
[500:1,000) 0.113 0.174
Emerging Markets Developed Markets
Any Bucket Size 6.898 3.411
(0:300) 7.837 3.470
[300:500) 11.669 7.389
[500:1,000) 8.854 5.751
Appendix Table 10Frequency of Bond Issuances
This table reports the mean number of issuances and the duration between issuances forinternational U.S. dollar-denominated bonds of any bucket size, with face values below$300 (0:300), between $300 and below $500 [300:500), and equal to or above $500[500:1,000) million, by firms in emerging markets (EM) and investment grade firms indeveloped markets (DM) during 2000-16. The analysis is restricted to firms that issued therelevant type of bond at least once during the sample period. Panel A reports the numberof issuances per year as follows: (1) the total number of bonds issued are summed per firm-year observation, (2) the mean number of issuances are then computed per firm, (3) themean firm is computed. Panel B reports the number of years between bond issuances onaverage. The values are computed as one over the respective values in Panel A.
Panel A. Number of Issuances per Year
Panel B. Years Between Issuances